In April 2025, at the ITER Private Sector Fusion Workshop in Cadarache, something remarkable unfolded. In a room filled with scientists, engineers and software visionaries, the line between big science and commercial innovation began to blur.   Three organisations – Microsoft Research, Arena and Brigantium Engineering – shared how artificial intelligence (AI), already transforming everything from language models to logistics, is now stepping into a new role: helping humanity to unlock the power of nuclear fusion.  Each presenter addressed a different part of the puzzle, but the message was the same: AI isn’t just a buzzword anymore. It’s becoming a real tool – practical, powerful and indispensable – for big science and engineering projects, including fusion.  “If we think of the agricultural revolution and the industrial revolution, the AI revolution is next – and it’s coming at a pace which is unprecedented,” said Kenji Takeda, director of research incubations at Microsoft Research.  Microsoft’s collaboration with ITER is already in motion. Just a month before the workshop, the two teams signed a Memorandum of Understanding (MoU) to explore how AI can accelerate research and development. This follows ITER’s initial use of Microsoft technology to empower their teams. A chatbot in Azure OpenAI service was developed to help staff navigate technical knowledge, on more than a million ITER documents, using natural conversation. GitHub Copilot assists with coding, while AI helps to resolve IT support tickets – those everyday but essential tasks that keep the lights on.  But Microsoft’s vision goes deeper. Fusion demands materials that can survive extreme conditions – heat, radiation, pressure – and that’s where AI shows a different kind of potential. MatterGen, a Microsoft Research generative AI model for materials, designs entirely new materials based on specific properties. “It’s like ChatGPT,” said Takeda, “but instead of ‘Write me a poem’, we ask it to design a material that can survive as the first wall of a fusion reactor.”  The next step? MatterSim – a simulation tool that predicts how these imagined materials will behave in the real world. By combining generation and simulation, Microsoft hopes to uncover materials that don’t yet exist in any catalogue.  While Microsoft tackles the atomic scale, Arena is focused on a different challenge: speeding up hardware development. As general manager Michael Frei put it: “Software innovation happens in seconds. In hardware, that loop can take months – or years.”  Arena’s answer is Atlas, a multimodal AI platform that acts as an extra set of hands – and eyes – for engineers. It can read data sheets, interpret lab results, analyse circuit diagrams and even interact with lab equipment through software interfaces. “Instead of adjusting an oscilloscope manually,” said Frei, “you can just say, ‘Verify the I2C [inter integrated circuit] protocol’, and Atlas gets it done.”  It doesn’t stop there. Atlas can write and adapt firmware on the fly, responding to real-time conditions. That means tighter feedback loops, faster prototyping and fewer late nights in the lab. Arena aims to make building hardware feel a little more like writing software – fluid, fast and assisted by smart tools.  Fusion, of course, isn’t just about atoms and code – it’s also about construction. Gigantic, one-of-a-kind machines don’t build themselves. That’s where Brigantium Engineering comes in. Founder Lynton Sutton explained how his team uses “4D planning” – a marriage of 3D CAD models and detailed construction schedules – to visualise how everything comes together over time. “Gantt charts are hard to interpret. 3D models are static. Our job is to bring those together,” he said.  The result is a time-lapse-style animation that shows the construction process step by step. It’s proven invaluable for safety reviews and stakeholder meetings. Rather than poring over spreadsheets, teams can simply watch the plan come to life.  And there’s more. Brigantium is bringing these models into virtual reality using Unreal Engine – the same one behind many video games. One recent model recreated ITER’s tokamak pit using drone footage and photogrammetry. The experience is fully interactive and can even run in a web browser. “We’ve really improved the quality of the visualisation,” said Sutton. “It’s a lot smoother; the textures look a lot better. Eventually, we’ll have this running through a web browser, so anybody on the team can just click on a web link to navigate this 4D model.”  Looking forward, Sutton believes AI could help automate the painstaking work of syncing schedules with 3D models. One day, these simulations could reach all the way down to individual bolts and fasteners – not just with impressive visuals, but with critical tools for preventing delays.  Despite the different approaches, one theme ran through all three presentations: AI isn’t just a tool for office productivity. It’s becoming a partner in creativity, problem-solving and even scientific discovery.  Takeda mentioned that Microsoft is experimenting with “world models” inspired by how video games simulate physics. These models learn about the physical world by watching pixels in the form of videos of real phenomena such as plasma behaviour. “Our thesis is that if you showed this AI videos of plasma, it might learn the physics of plasmas,” he said.  It sounds futuristic, but the logic holds. The more AI can learn from the world, the more it can help us understand it – and perhaps even master it. At its heart, the message from the workshop was simple: AI isn’t here to replace the scientist, the engineer or the planner; it’s here to help, and to make their work faster, more flexible and maybe a little more fun. As Takeda put it: “Those are just a few examples of how AI is starting to be used at ITER. And it’s just the start of that journey.”  If these early steps are any indication, that journey won’t just be faster – it might also be more inspired. 

The chaotic combat in Zenless Zone Zero combines both frantic button mashing with team-swap tactics, measured ability usage, and a number of defensive options to keep the players' Agents alive. One such defensive option is the Defensive Assist, which is one of the staples of the game's combat and one of the best things you can do while fighting Ethereals and other undesirables.

More enterprises have moved to pay-for-performance salary and promotion models that measure progress toward goals -- but how do you measure goals for a maintenance programmer who barrels through a request backlog but delivers marginal value for the business, or for a business analyst whose success is predicated on forging intangibles like trust and cooperation with users so things can get done? It’s an age-old question facing companies, now that 77% of them use some type of pay-for-performance model. What are some popular pay-for-performance use cases? A factory doing piece work that pays employees based upon the number of items they assemble. A call center that pays agents based on how many calls they complete per day. A bank teller who gets rewarded for how many customers they sign up for credit cards. An IT project team that gets a bonus for completing a major project ahead of schedule. The IT example differs from the others, because it depends on team and not individual execution, but there nevertheless is something tangible to measure. The other use cases are more clearcut -- although they don’t account for pieces in the plant that were poorly assembled in haste to make quota and had to be reworked, or a call center agent who pushes calls off to someone else so they can end their calls in six minutes or less, or the teller who signs up X number of customers for credit cards, although two-thirds of them never use the credit card they signed up for. Related:In short, there are flaws in pay-for-performance models just as there are in other types of compensation models that organizations use. So, what’s the best path for IT for CIOs who want to implement pay for performance? One approach is to measure pay for performance based upon four key elements: hard results, effort, skill, and communications. The mix of these elements will vary, depending on the type of position each IT staff member performs. Here are two examples of pay per performance by position: 1. Computer maintenance programmers and help desk specialists Historically, IT departments have used hard numbers like how many open requests a computer maintenance programmer has closed, or how many calls a help desk employee has solved. There is merit in using hard results, and hard results should be factored into performance reviews for these individuals -- but hard numbers don’t tell the whole story.  For example, how many times has a help desk agent gone the extra mile with a difficult user or software bug, taking the time to see the entire process through until it is thoroughly solved? lf the issue was of a global nature, did the Help Desk agent follow up by letting others who use the application know that a bug was fixed? For the maintenance programmer who has completed the most open requests, which of these requests really solved a major business pain point? For both help desk and maintenance programming employees, were the changes and fixes properly documented and communicated to everyone with a need to know? And did these employees demonstrate the skills needed to solve their issues? Related:It’s difficult to capture hard results on elements like effort, communication and skills, but one way to go about it is to survey user departments on individual levels of service and effectiveness. From there, it’s up to IT managers to determinate the “mix” of hard results, effort, communication and skills on which the employee will be evaluated, and to communicate upfront to the employee what the pay for performance assessment will be based on. 2. Business analysts and trainers Business analysts and trainers are difficult to quantify in pay for performance models because so much of their success depends upon other people. A business analyst can know everything there is to know about a particular business area and its systems, but if the analyst is working with unresponsive users, or lacks the soft skills needed to communicate with users, the pay for performance can’t be based upon the technology skillset alone.  Related:IT trainers face a somewhat different dilemma when it  comes to performance evaluation: they can produce the training that new staff members need before staff is deployed on key projects,  but if a project gets delayed and this causes trainees to lose the knowledge that they learned, there is little the trainer can do aside from offering a refresher course. Can pay for performance be used for positions like these? It’s a mixed answer. Yes, pay per performance can be used for trainers, based upon how many individuals the trainer trains and how many new courses the trainer obtains or develops. These are the hard results. However, since so much of training’s execution depends upon other people downstream, like project managers who must start projects on time so new skills aren’t lost,  managers of training should also consider pay for performance elements such as effort (has the trainer consistently gone the extra mile to make things work?), skills and communication.  In sum, for both business analysts and trainers, there are hard results that can be factored into a pay for performance formula, but there is also a need to survey each position’s “customers” -- those individuals (and their managers) who utilized the business analyst’s or trainer’s skills and products to accomplish their respective objectives in projects and training. Were these user-customers satisfied?  Summary Remarks The value that IT employees contribute to overall IT and to the business at large is a combination of tangible and intangible results. Pay for performance models are well suited to gauge tangible outcomes, but they fall short when it comes to the intangibles that could be just as important. Many years ago, when Pat Riley was coaching the Los Angeles Lakers, an interviewer asked what type of metrics he used when he measured the effectiveness of individual players on the basketball court. Was it the number of points, rebounds, or assists? Riley said he used an “effort" index. For example, how many times did a player go up to get a rebound, even if he didn’t end up with the ball? Riley said the effort individual players exhibited mattered, because even if they didn’t get the rebound, they were creating situations so someone else on the team could. IT is similar. It’s why OKR International, a performance consultancy, stated “Intangibles often create or destroy value quietly -- until their impact is too big to ignore. In the long run, they are the unseen levers that determine whether strategy thrives or withers.”  What CIOs and IT leadership can do when they use pay for performance is to assure that hard results, effort, communications and skills are appropriately blended for each IT staff position, and its responsibilities and realities -- because you can’t attach a numerical measurement to everything -- but you can observe visible changes that begin to manifest when a business analyst turns around what has been a hostile relationship with a user department and you begin to get things done. 

F1 25 is a near-perfect mix of realism and playability that offers much of the drama from the real-life sport and a sprinkling of fiction and, uh, Brad Pitt just becauseTech18:11, 02 Jun 2025This year's game looks better than everIn many ways, writing an F1 25 review should be the easiest of this year’s critical assessments. Codemasters is legendary for its commitment to digital recreations of automotive competition (I’ve been playing its games since TOCA on the PS1 ), and having the F1 licence means it’ll always be cutting-edge in terms of racers, tracks, and more.If you’re an F1 fan, you’ve almost certainly already bought it, and while non-fans of sports games will baulk at paying for a “roster update” each year, Codemasters simply refuses to coast, keeping its foot firmly on the gas and moving from last year’s podium finish to Championship-winning form with this year’s entry.‌Conditions can be treacherous‌Last year’s F1 24 was easily one of the most impressive games to look at on PS5 Pro, and while Codemasters had talked a good game about visual fidelity , I wasn’t sure it would be able to take much of a realistic step beyond.And yet, F1 25 is frequently stunning. In motion, it’s hard to see anything wholly new, but that’s more down to the speed at which you’ll be taking corners of meticulously detailed tracks. Slow things down a tad, though, and you’ll find things a little less sterile than they had been.Whereas F1 24 circuits felt a little too clean at times, there’s a little more dirt here and there, more wear on the track, and even correctly identified tree species in tracks that have been scanned via LiDAR.Article continues belowIt’s likely an ongoing process, with Bahrain, Miami, Melbourne, Suzuka and Imola getting the scanned treatment so far, but it’s an impressive taste of what’s to come and could mean upcoming games look even better.On track, the handling model feels much breezier. You can still crank up the difficulty by leaving the assists in the pits, but cars feel more responsive than ever.You’ll need that, too, because some tracks can be driven in reverse (complete with mirrored pit lanes).‌You can still race, but you'll pick one of your stars to "follow" for the weekendThe crown jewel of this year’s entry, however, is My Team. The mode has always been solid, but lacking in ambition, but this year sees Codemasters really go to town on its underlying machinery.While you’ll no longer be some team owner/driver hybrid superstar like Tony Stark in Iron Man 2, that adds an interesting new flavour to the mode. You’ll start as boss of an existing team or form your own, and then hire drivers, work to improve your car, and try to woo sponsors.‌Because you’re no longer racing yourself, there are more magnanimous decisions to be made about car parts. Research costs time, manufacturing costs money, and then you’re left to decide which of your racers gets the added boost.Consistently upsetting one can see them look elsewhere, while you can plan for next season’s drivers right from the off, making a Lewis Hamilton-esque switch to a rival a pressing concern throughout the year.While much of My Team takes place in menus, they all feel dynamic enough to feel much more enjoyable than you might expect, and while it doesn’t get quite as deep as F1 Manager, it’s still full of potential.‌You can even sneak some star power onto the grid, too, taking the reins of Brad Pitt’s racing team from the upcoming F1 movie , or signing iconic former drivers to build a dream lineup.As an aside, I love that EA is experimenting with things like this in its career modes, especially since EA FC added Icons to its own version. Long may it continue.Konnersport are now vying for titles(Image: EA)‌Another big return this year comes from Braking Point , marking its third instalment. The mode that essentially condenses a season’s worth of drama into playable chunks with a healthy dose of inspiration from Netflix ’s Drive to Survive is back as part of its “one season off, one season on” cadence.It’s packed with sporting cliches and no small amount of cheese, but it humanises a sport that can sometimes feel more focuses on cutting seconds off a lap than it can the drivers doing that work.After years of building a team, Konnersport is finally competing for the Championship, and players can switch between their driver roster to achieve different objectives, and there’s an alternative ending for those willing to commit.Article continues belowF1 25 is the best entry in years, with changes big and small piling up to offer a truly immersive and feature-packed title.My Team will get the plaudits (and rightfully so), but Braking Point’s return and Codemasters’ continued commitment to realism shouldn’t be forgotten.Reviewed on PS5 Pro. Review copy provided by the publisher.‌‌‌

It was rumored for many months and somewhat a given with the launch of the Definitive Edition, but NetherRealm Studios has finally confirmed it. Mortal Kombat 1 won’t receive any more DLC characters or story chapters. While balance adjustments and fixes will continue, the developer will focus on its next project to “make it as great as we possibly can,” though it understands the news will disappoint fans. Launched in September 2023 for Xbox Series X/S, PS5, PC, and Nintendo Switch, Mortal Kombat 1 is a reboot of the series timeline. It introduced Kameo Fighters as assists and a new mode, Invasions, to accommodate a seasonal format. While it received acclaim for its visuals and story, fans raised concerns about the mechanics, extensive bugs and excessive monetization. An expansion, Khaos Reigns, followed in September 2024 alongside Kombat Pack 2, but this also received criticism for its short story and $50 price. Another expansion and Kombat Pack 3 were allegedly in development but seemingly cancelled due to Khaos Reigns’ poor sales. As for the future, NetherRealm Studios may be working on a new Injustice, according to a MultiVersus leaker. Nothing is confirmed, so stay tuned for updates. We are hearing players’ requests for continued game support of Mortal Kombat 1, and, while we will continue to support Mortal Kombat 1 through balance adjustments and fixes, there will not be additional DLC characters or story chapters released from this point on.— Mortal Kombat 1 (@MortalKombat) May 23, 2025 We understand this will be disappointing for fans, but our team at NetherRealm needs to shift focus to the next project in order to make it as great as we possibly can.— Mortal Kombat 1 (@MortalKombat) May 23, 2025

Senior Administrative Assistant, Quality Assurance | Austin, TXBlizzard EntertainmentAustin Texas 78717 United States of America1 hour agoApplyTeam Name:Quality AssuranceJob Title:Senior Administrative Assistant, Quality Assurance | Austin, TXRequisition ID:R025237Job Description:The concept of “Blizzard polish,” that is, the infinite care and loving detail put into every aspect of our games, is something we take seriously and pride ourselves on delivering to our players. It’s a responsibility shared across the company – and its undisputed heart and soul is Blizzard Entertainment Quality Assurance (QA). Blizzard QA is a close-knit team; we care about problem solving, providing opportunities for professional growth, and succeeding together. We genuinely love what we do for a living and expect the same from everyone who joins us. If your career is ready for its next great challenge, get in touch and let’s get to work!The Administrative Assistant role provides crucial support to team leadership, involving managing complex calendars, coordinating travel arrangements, and assisting with meeting preparation. They will be proactive, possess exceptional communication skills, and thrive in a fast-paced environment. Responsibilities include offering confidential support, acting as a communication bridge, supporting event planning, providing general administrative assistance to leadership, and being a resource to the overall employee population. Additionally, the Administrative Assistant will manage calendars, arrange travel, and foster positive internal and external relationships.This role is anticipated to be a hybrid work position, with some work on-site and some work-from-home. The potential home studio for this role is Austin, TX.Responsibilities:Provide confidential administrative support to leadership, offering clerical and administrative assistance and following up with staff for information or support as needed.Coordinate travel arrangements, including securing passports and visas. Manage travel logistics, hotel accommodations, and meeting planning for staff as needed.Manage meeting and event logistics, including room setup, sending calendar invites, tracking RSVPs, preparing agendas, and taking comprehensive meeting notes. Provide detailed summaries of team meetings, ensuring all key points and action items are captured accurately.Share established policies, procedures, and guidelines in response to inquiries. Reach out to appropriate stakeholders for support and to make recommendations to support the employee experience.Provide administrative support to the leadership team, including scheduling meetings, maintaining calendars, drafting correspondence, creating spreadsheets and presentations, preparing expense reports and documents, and managing files.Work collaboratively and manage relationships with external vendors.Assists with ordering, stocking, maintenance, and disposal of office supplies and equipment.Assist in coordinating setup and moves of workstations, desks, offices, and equipment for existing and new employees.Assist in onboarding new employees to the team and coordinating their onboarding meetings and activities.Support in planning, organizing, and executing site events and activities for the enjoyment of all employees, including holiday events, diversity and inclusion, or local events.Foster positive relationships with staff and management, both within and outside the company, and ensure efficient collaboration.Performs other duties as assigned.MINIMUM REQUIREMENTSExperienceMinimum of 4 years of administrative support experience.Experience supporting teams in various time zones.Experience planning and coordinating events, such as team meetings, conferences, or company-wide events.Experience booking travel, lodging accommodations, and expense management.Knowledge & SkillsHigh School diploma or equivalent required. Bachelor’s Degree in a related field preferred.Proven ability to provide confidentiality and discretion and act with excellent judgment.Strong organizational skills, detail orientation, and world-class prioritization and multi-tasking.Proficient in Microsoft Office Suite and other office productivity tools.Ability to work effectively in a global, cross-functional team environment.Demonstrable ability to own and handle multiple projects simultaneously and the ability to juggle multiple tasks and priorities simultaneously can be a valuable skill.Excellent written and verbal communication, planning, organization, and time management skills.EXTRA POINTSExperience in the technology industry or Quality Assurance field.Knowledge of project management principles and practices.Passion for Blizzard’s line of products and services.Your PlatformBest known for iconic video game universes including Warcraft®, Overwatch®, Diablo®, and StarCraft®, Blizzard Entertainment, Inc. (www.blizzard.com), a division of Activision Blizzard, which was acquired by Microsoft (NASDAQ: MSFT), is a premier developer and publisher of entertainment experiences. Blizzard Entertainment has created some of the industry’s most critically acclaimed and genre-defining games over the last 30 years, with a track record that includes multiple Game of the Year awards. Blizzard Entertainment engages tens of millions of players around the world with titles available on PC via Battle.net®, Xbox, PlayStation, Nintendo Switch, iOS, and Android.Our WorldActivision Blizzard, Inc., is one of the world's largest and most successful interactive entertainment companies and is at the intersection of media, technology and entertainment. We are home to some of the most beloved entertainment franchises including Call of Duty®, World of Warcraft®, Overwatch®, Diablo®, Candy Crush™ and Bubble Witch™. Our combined entertainment network delights hundreds of millions of monthly active users in 196 countries, making us the largest gaming network on the planet!Our ability to build immersive and innovative worlds is only enhanced by diverse teams working in an inclusive environment. We aspire to have a culture where everyone can thrive in order to connect and engage the world through epic entertainment. We provide a suite of benefits that promote physical, emotional and financial well-being for ‘Every World’ - we’ve got our employees covered!The videogame industry and therefore our business is fast-paced and will continue to evolve. As such, the duties and responsibilities of this role may be changed as directed by the Company at any time to promote and support our business and relationships with industry partners.We love hearing from anyone who is enthusiastic about changing the games industry. Not sure you meet all qualifications? Let us decide! Research shows that women and members of other under-represented groups tend to not apply to jobs when they think they may not meet every qualification, when, in fact, they often do! We are committed to creating a diverse and inclusive environment and strongly encourage you to apply.We are committed to working with and providing reasonable assistance to individuals with physical and mental disabilities. If you are a disabled individual requiring an accommodation to apply for an open position, please email your request to accommodationrequests@activisionblizzard.com. General employment questions cannot be accepted or processed here. Thank you for your interest.We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity, age, marital status, veteran status, or disability status, among other characteristics.RewardsWe provide a suite of benefits that promote physical, emotional and financial well-being for ‘Every World’ - we’ve got our employees covered! Subject to eligibility requirements, the Company offers comprehensive benefits including:Medical, dental, vision, health savings account or health reimbursement account, healthcare spending accounts, dependent care spending accounts, life and AD&D insurance, disability insurance;401(k) with Company match, tuition reimbursement, charitable donation matching;Paid holidays and vacation, paid sick time, floating holidays, compassion and bereavement leaves, parental leave;Mental health & wellbeing programs, fitness programs, free and discounted games, and a variety of other voluntary benefit programs like supplemental life & disability, legal service, ID protection, rental insurance, and others;If the Company requires that you move geographic locations for the job, then you may also be eligible for relocation assistance.Eligibility to participate in these benefits may vary for part time and temporary full-time employees and interns with the Company. You can learn more by visiting https://www.benefitsforeveryworld.com/ .In the U.S., the standard base pay range for this role is $20.77 - $38.46 Hourly. These values reflect the expected base pay range of new hires across all U.S. locations. Ultimately, your specific range and offer will be based on several factors, including relevant experience, performance, and work location. Your Talent Professional can share this role’s range details for your local geography during the hiring process. In addition to a competitive base pay, employees in this role may be eligible for incentive compensation. Incentive compensation is not guaranteed. While we strive to provide competitive offers to successful candidates, new hire compensation is negotiable. Create Your Profile — Game companies can contact you with their relevant job openings. Apply

Posted on : May 21, 2025 By Tech World Times Business  Rate this post The autocratic leadership style is a way in which a leader holds significant decision-making authority in the industry. The leader under this leadership style expects stringent compliance from the subordinates. The autocratic leader takes the majority of the decisions independently, without seeking any sort of input from the team. This approach is heavily criticized by the majority of the people. However, there are some advantages. Keeping this scenario under consideration, we are presenting to you some reasons why an autocratic style can drive results and efficiency. It Makes The Processes Efficient Autocratic leaders can make quick decisions. Numerous studies say bad decisions are better than no decisions. This is because you at least reach some sort of conclusion. It plays an imperative role in avoiding delays due to the extended decision-making process. It is specifically beneficial in time-sensitive situations. There is a clear Hierarchy and Decision-Making Authority The decision-making authority and clear hierarchy in autocratic leadership assist in communicating and setting clear objectives and expectations. This plays a vital role in reducing confusion among team members. The Leader Is Accountable For Everything This leadership style makes the leader accountable for every decision because other people are not allowed to participate. This improves responsibility and transparency in the organization. It also Allows To Maintain The Order In a situation of chaos and crisis, autocratic leaders can restore stability and order rapidly by making decisions with full authority. Autocratic Leaders Have Expertise In Their Domain Autocratic leaders have expertise in their domain. They can make informed decisions that are based on their knowledge and experience. It can serve as an asset to the company. This Style Is Effective In Emergencies Autocratic leadership outshines during emergencies. This is the point where quick action is required to be taken. At this point, decisions are made without any lengthy meeting leading to time wastage. This also Ensures Consistency Autocratic leaders guarantee that decisions are consistent with the company’s goal and vision. This helps to maintain a steady course. It helps to Main Focus Autocratic leaders help keep attention on the company’s important objectives. This decreases the risk of diverging priorities and distractions. It Assists In Effective Risk Management Autocratic leaders are risk averse. This prevents ill-considered or impulsive decisions that perhaps arise because of democratic environments. It Promotes Decisiveness The autocratic leadership style can foster a decisive mindset within the team. This makes them habitual in making quick decisions when required. Conclusion: In some situations, an autocratic leadership style is extremely beneficial. For example, in sports or military operations decisions are required to be taken on shorter notice. There is no time to take opinions from other people. But in other cases, it is the complete opposite. It is perhaps not suitable for all organizational cultures and situations. Leaders are required to balance the advantages of clear decision-making with possible drawbacks of lesser employee involvement and decreased motivation. A good leader is required to adapt their style based on the requirements of their team and the demands of the situation.  Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

Call it the ultimate proving ground. Collaborating with teammates in the modern workplace requires fast, fluid thinking. Providing insights quickly, while juggling webcams and office messaging channels, is a startlingly good test, and enterprise AI is about to pass it — just in time to provide assistance to busy knowledge workers. To support enterprises in boosting productivity with AI teammates, NVIDIA today introduced a new NVIDIA Enterprise AI Factory validated design at COMPUTEX. IT teams deploying and scaling AI agents can use the design to build accelerated infrastructure and easily integrate with platforms and tools from NVIDIA software partners. NVIDIA also unveiled new NVIDIA AI Blueprints to aid developers building smart AI teammates. Using the new blueprints, developers can enhance employee productivity through adaptive avatars that understand natural communication and have direct access to enterprise data. Blueprints for Engaging, Insightful AI Agents Enterprises can use NVIDIA’s latest AI Blueprints to create agents that align with their business objectives. Using the Tokkio NVIDIA AI Blueprint, developers can create interactive digital humans that can respond to emotional and contextual cues, while the AI-Q blueprint enables queries of many data sources to infuse AI agents with the company’s knowledge and gives them intelligent reasoning capabilities. Building these intelligent AI agents is a full-stack challenge. These blueprints are designed to run on NVIDIA’s accelerated computing infrastructure — including data centers built with the universal NVIDIA RTX PRO 6000 Server Edition GPU, which is part of NVIDIA’s vision for AI factories as complete systems for creating and putting AI to work. The Tokkio blueprint simplifies building interactive AI agent avatars for more natural and humanlike interactions. These AI agents are designed for intelligence. They integrate with foundational blueprints including the AI-Q NVIDIA Blueprint, part of the NVIDIA AI Data Platform, which uses retrieval-augmented generation and NVIDIA NeMo Retriever microservices to access enterprise data. AI Agents Boost People’s Productivity Customers around the world are already using these AI agent solutions. At the COACH Play store on Cat Street in Harajuku, Tokyo, imma provides an interactive in-store experience and gives personalized styling advice through natural, real-time conversation. Marking COACH’s debut in digital humans and AI-driven retail, the initiative merges cutting-edge technology with fashion to create an immersive and engaging customer journey. Developed by Aww Inc. and powered by NVIDIA ACE, the underlying technology that makes up the Tokkio blueprint, imma delivers lifelike interactions and tailored style suggestions. The experience allows for dynamic, unscripted conversations designed to connect with visitors on a personal level, highlighting COACH’s core values of courage and self-expression. “Through this groundbreaking innovation in the fashion retail space, customers can now engage in real-time, free-flowing conversations with our iconic virtual human, imma — an AI-powered stylist — right inside the store in the heart of Harajuku,” said Yumi An King, executive director of Aww Inc. “It’s been inspiring to see visitors enjoy personalized styling advice and build a sense of connection through natural conversation. We’re excited to bring this vision to life with NVIDIA and continue redefining what’s possible at the intersection of AI and fashion.” Watch how Aww Inc. is leveraging the latest Tokkio NVIDIA AI Blueprint in its AI-powered virtual human stylist, imma, to connect with shoppers through natural conversation and provide personalized styling advice.  Royal Bank of Canada developed Jessica, an AI agent avatar that assists employees in handling reports of fraud. With Jessica’s help, bank employees can access the most up-to-date information so they can handle fraud reports faster and more accurately, enhancing client service. Ubitus and the Mackay Memorial Hospital, located in Taipei, are teaming up to make hospital visits easier and friendlier with the help of AI-powered digital humans. These lifelike avatars are created using advanced 8K facial scanning and brought to life by Ubitus’ AI model integrated with NVIDIA ACE technologies, including NVIDIA Audio2Face 3D for expressions and NVIDIA Riva for speech. Deployed on interactive touchscreens, these digital humans offer hospital navigation, health education and registration support — reducing the burden on frontline staff. They also provide emotional support in pediatric care, aimed at reducing anxiety during wait times. Ubitus and the Mackay Memorial Hospital are making hospital visits easier and friendlier with the help of NVIDIA AI-powered digital humans. Cincinnati Children’s Hospital is exploring the potential of digital avatar technology to enhance the pediatric patient experience. As part of its ongoing innovation efforts, the hospital is evaluating platforms such as NVIDIA’s Digital Human Blueprint to inform the early design of “Care Companions” — interactive, friendly avatars that could help young patients better understand their healthcare journey. “Children can have a lot of questions about their experiences in the hospital, and often respond more to a friendly avatar, like stylized humanoids, animals or robots, that speaks at their level of understanding,” said Dr. Ryan Moore, chief of emerging technologies at Cincinnati Children’s Hospital. “Through our Care Companions built with NVIDIA AI, gamified learning, voice interaction and familiar digital experiences, Cincinnati Children’s Hospital aims to improve understanding, reduce anxiety and support lifelong health for young patients.” This early-stage exploration is part of the hospital’s broader initiative to evaluate new and emerging technologies that could one day enhance child-centered care. Software Platforms Support Agents on AI Factory Infrastructure  AI agents are one of the many workloads driving enterprises to reimagine their data centers as AI factories built for modern applications. Using the new NVIDIA Enterprise AI Factory validated design, enterprises can build data centers that provide universal acceleration for agentic AI, as well as design, engineering and business operations. The Enterprise AI Factory validated design features support for software tools and platforms from NVIDIA partners, making it easier to build and run generative and agent-based AI applications. Developers deploying AI agents on their AI factory infrastructure can tap into partner platforms such as Dataiku, DataRobot, Dynatrace and JFrog to build, orchestrate, operationalize and scale AI workflows. The validated design supports frameworks from CrewAI, as well as vector databases from DataStax and Elastic, to help agents store, search and retrieve data. With tools from partners including Arize AI, Galileo, SuperAnnotate, Unstructured and Weights & Biases, developers can conduct data labeling, synthetic data generation, model evaluation and experiment tracking. Orchestration and deployment partners including Canonical, Nutanix and Red Hat support seamless scaling and management of AI agent workloads across complex enterprise environments. Enterprises can secure their AI factories with software from safety and security partners including ActiveFence, CrowdStrike, Fiddler, Securiti and Trend Micro. The NVIDIA Enterprise AI Factory validated design and latest AI Blueprints empower businesses to build smart, adaptable AI agents that enhance productivity, foster collaboration and keep pace with the demands of the modern workplace. See notice regarding software product information.

A shared language for product teams to build usable, intelligent and safe GenAI experiences beyond just the modelGenerative AI introduces a new way for humans to interact with systems by focusing on intent-based outcome specification. GenAI introduces novel challenges because its outputs are probabilistic, requires understanding of variability, memory, errors, hallucinations and malicious use which brings an essential need to build principles and design patterns as described by IBM.Moreover, any AI product is a layered system where LLM is just one ingredient and memory, orchestration, tool extensions, UX and agentic user-flows builds the real magic!This article is my research and documentation of evolving GenAI design patterns that provide a shared language for product managers, data scientists, and interaction designers to create products that are human-centred, trustworthy and safe. By applying these patterns, we can bridge the gap between user needs, technical capabilities and product development process.Here are 21 GenAI UX patternsGenAI or no GenAIConvert user needs to data needsAugment or automateDefine level of automationProgressive AI adoptionLeverage mental modelsConvey product limitsDisplay chain of thought (CoT)Leverage multiple outputsProvide data sourcesConvey model confidenceDesign for memory and recallProvide contextual input parametersDesign for coPilot, co-Editing or partial automationDefine user controls for AutomationDesign for user input error statesDesign for AI system error statesDesign to capture user feedbackDesign for model evaluationDesign for AI safety guardrailsCommunicate data privacy and controls1. GenAI or no GenAIEvaluate whether GenAI improves UX or introduces complexity. Often, heuristic-based (IF/Else) solutions are easier to build and maintain.Scenarios when GenAI is beneficialTasks that are open-ended, creative and augments user.E.g., writing prompts, summarizing notes, drafting replies.Creating or transforming complex outputs (e.g., images, video, code).E.g., converting a sketch into website code.Where structured UX fails to capture user intent.Scenarios when GenAI should be avoidedOutcomes that must be precise, auditable or deterministic. E.g., Tax forms or legal contracts.Users expect clear and consistent information.E.g. Open source software documentationHow to use this patternDetermine the friction points in the customer journeyAssess technology feasibility: Determine if AI can address the friction point. Evaluate scale, dataset availability, error risk assessment and economic ROI.Validate user expectations: - Determine if the AI solution erodes user expectations by evaluating whether the system augments human effort or replaces it entirely, as outlined in pattern 3, Augment vs. automate. - Determine if AI solution erodes pattern 6, Mental models2. Convert user needs to data needsThis pattern ensures GenAI development begins with user intent and data model required to achieve that. GenAI systems are only as good as the data they’re trained on. But real users don’t speak in rows and columns, they express goals, frustrations, and behaviours. If teams fail to translate user needs into structured, model-ready inputs, the resulting system or product may optimise for the wrong outcomes and thus user churn.How to use this patternCollaborate as a cross-functional team of PMs, Product designers and Data Scientists and align on user problems worth solving.Define user needs by using triangulated research: Qualitative (Market Reports, Surveys or Questionnaires) + Quantitative (User Interviews, Observational studies) + Emergent (Product reviews, Social listening etc.) and synthesising user insights using JTBD framework, Empathy Map to visualise user emotions and perspectives. Value Proposition Canvas to align user gains and pains with featuresDefine data needs and documentation by selecting a suitable data model, perform gap analysis and iteratively refine data model as needed. Once you understand the why, translate it into the what for the model. What features, labels, examples, and contexts will your AI model need to learn this behaviour? Use structured collaboration to figure out.3. Augment vs automateOne of the critical decisions in GenAI apps is whether to fully automate a task or to augment human capability. Use this pattern to to align with user intent and control preferences with the technology.Automation is best for tasks users prefer to delegate especially when they are tedious, time-consuming or unsafe. E.g., Intercom FinAI automatically summarizes long email threads into internal notes, saving time on repetitive, low-value tasks.Augmentation enhances tasks users want to remain involved in by increasing efficiency, increase creativity and control. E.g., Magenta Studio in Abelton support creative controls to manipulate and create new music.How to use this patternTo select the best approach, evaluate user needs and expectations using research synthesis tools like empathy map (visualise user emotions and perspectives) and value proposition canvas (to understand user gains and pains)Test and validate if the approach erodes user experience or enhances it.4. Define level of automationIn AI systems, automation refers to how much control is delegated to the AI vs user. This is a strategic UX pattern to decide degree of automation based upon user pain-point, context scenarios and expectation from the product.Levels of automationNo automation (AI assists but user decides)The AI system provides assistance and suggestions to the user but requires the user to make all the decisions. E.g., Grammarly highlights grammar issues but the user accepts or rejects corrections.Partial automation/ co-pilot/ co-editor (AI acts with user oversight)The AI initiates actions or generates content, but the user reviews or intervenes as needed. E.g., GitHub Copilot suggest code that developers can accept, modify, or ignore.Full automation (AI acts independently)The AI system performs tasks without user intervention, often based on predefined rules, tools and triggers. Full automation in GenAI are often referred to as Agentic systems. E.g., Ema can autonomously plan and execute multi-step tasks like researching competitors, generating a report and emailing it without user prompts or intervention at each step.How to use this patternEvaluate user pain point to be automated and risk involved: Automating tasks is most effective when the associated risk is low without severe consequences in case of failure. Low-risk tasks such as sending automated reminders, promotional emails, filtering spam emails or processing routine customer queries can be automated with minimal downside while saving time and resources. High-risk tasks such as making medical diagnoses, sending business-critical emails, or executing financial trades requires careful oversight due to the potential for significant harm if errors occur.Evaluate and design for particular automation level: Evaluate if user pain point should fall under — No Automation, Partial Automation or Full Automation based upon user expectations and goals.Define user controls for automation (refer pattern 15)5. Progressive GenAI adoptionWhen users first encounter a product built on new technology, they often wonder what the system can and can’t do, how it works and how they should interact with it.This pattern offers multi-dimensional strategy to help user onboard an AI product or feature, mitigate errors, aligns with user readiness to deliver an informed and human-centered UX.How to use this patternThis pattern is a culmination of many other patternsFocus on communicating benefits from the start: Avoid diving into details about the technology and highlight how the AI brings new value.Simplify the onboarding experience Let users experience the system’s value before asking data-sharing preferences, give instant access to basic AI features first. Encourage users to sign up later to unlock advanced AI features or share more details. E.g., Adobe FireFly progressively onboards user with basic to advance AI featuresDefine level of automation (refer pattern 4) and gradually increase autonomy or complexity.Provide explainability and trust by designing for errors (refer pattern 16 and 17).Communicate data privacy and controls (refer pattern 21) to clearly convey how user data is collected, stored, processed and protected.6. Leverage mental modelsMental models help user predict how a system (web, application or other kind of product) will work and, therefore, influence how they interact with an interface. When a product aligns with a user’s existing mental models, it feels intuitive and easy to adopt. When it clashes, it can cause frustration, confusion, or abandonment​.E.g. Github Copilot builds upon developers’ mental models from traditional code autocomplete, easing the transition to AI-powered code suggestionsE.g. Adobe Photoshop builds upon the familiar approach of extending an image using rectangular controls by integrating its Generative Fill feature, which intelligently fills the newly created space.How to use this patternIdentifying and build upon existing mental models by questioningWhat is the user journey and what is user trying to do?What mental models might already be in place?Does this product break any intuitive patterns of cause and effect?Are you breaking an existing mental model? If yes, clearly explain how and why. Good onboarding, microcopy, and visual cues can help bridge the gap.7. Convey product limitsThis pattern involves clearly conveying what an AI model can and cannot do, including its knowledge boundaries, capabilities and limitations.It is helpful to builds user trust, sets appropriate expectations, prevents misuse, and reduces frustration when the model fails or behaves unexpectedly.How to use this patternExplicitly state model limitations: Show contextual cues for outdated knowledge or lack of real-time data. E.g., Claude states its knowledge cutoff when the question falls outside its knowledge domainProvide fallbacks or escalation options when the model cannot provide a suitable output. E.g., Amazon Rufus when asked about something unrelated to shopping, says “it doesn’t have access to factual information and, I can only assists with shopping related questions and requests”Make limitations visible in product marketing, onboarding, tooltips or response disclaimers.8. Display chain of thought (CoT)In AI systems, chain-of-thought (CoT) prompting technique enhances the model’s ability to solve complex problems by mimicking a more structured, step-by-step thought process like that of a human.CoT display is a UX pattern that improves transparency by revealing how the AI arrived at its conclusions. This fosters user trust, supports interpretability, and opens up space for user feedback especially in high-stakes or ambiguous scenarios.E.g., Perplexity enhances transparency by displaying its processing steps helping users understand the thoughtful process behind the answers.E.g., Khanmigo an AI Tutoring system guides students step-by-step through problems, mimicking human reasoning to enhance understanding and learning.How to use this patternShow status like “researching” and “reasoning to communicate progress, reduce user uncertainty and wait times feel shorter.Use progressive disclosure: Start with a high-level summary, and allow users to expand details as needed.Provide AI tooling transparency: Clearly display external tools and data sources the AI uses to generate recommendations.Show confidence & uncertainty: Indicate AI confidence levels and highlight uncertainties when relevant.9. Leverage multiple outputsGenAI can produce varied responses to the same input due to its probabilistic nature. This pattern exploits variability by presenting multiple outputs side by side. Showing diverse options helps users creatively explore, compare, refine or make better decisions that best aligns with their intent. E.g., Google Gemini provides multiple options to help user explore, refine and make better decisions.How to use this patternExplain the purpose of variation: Help users understand that differences across outputs are intentional and meant to offer choice.Enable edits: Let users rate, select, remix, or edit outputs seamlessly to shape outcomes and provide feedback. E.g., Midjourney helps user adjust prompt and guide your variations and edits using remix10. Provide data sourcesArticulating data sources in a GenAI application is essential for transparency, credibility and user trust. Clearly indicating where the AI derives its knowledge helps users assess the reliability of responses and avoid misinformation.This is especially important in high stakes factual domains like healthcare, finance or legal guidance where decisions must be based on verified data.How to use this patternCite credible sources inline: Display sources as footnotes, tooltips, or collapsible links. E.g., NoteBookLM adds citations to its answers and links each answer directly to the part of user’s uploaded documents.Disclose training data scope clearly: For generative tools (text, images, code), offer a simple explanation of what data the model was trained on and what wasn’t included. E.g., Adobe Firefly discloses that its Generative Fill feature is trained on stock imagery, openly licensed work and public domain content where the copyright has expired.Provide source-level confidence:In cases where multiple sources contribute, visually differentiate higher-confidence or more authoritative sources.11. Convey model confidenceAI-generated outputs are probabilistic and can vary in accuracy. Showing confidence scores communicates how certain the model is about its output. This helps users assess reliability and make better-informed decisions.How to use this patternAssess context and decision stakes: Showing model confidence depends on the context and its impact on user decision-making. In high-stakes scenarios like healthcare, finance or legal advice, displaying confidence scores are crucial. However, in low stake scenarios like AI-generated art or storytelling confidence may not add much value and could even introduce unnecessary confusion.Choose the right visualization: If design research shows that displaying model confidence aids decision-making, the next step is to select the right visualization method. Percentages, progress bars or verbal qualifiers (“likely,” “uncertain”) can communicate confidence effectively. The apt visualisation method depends on the application’s use-case and user familiarity. E.g., Grammarly uses verbal qualifiers like “likely” to the content it generated along with the userGuide user action during low confidence scenarios: Offer paths forward such as asking clarifying questions or offering alternative options.12. Design for memory and recallMemory and recall is an important concept and design pattern that enables the AI product to store and reuse information from past interactions such as user preferences, feedback, goals or task history to improve continuity and context awareness.Enhances personalization by remembering past choices or preferencesReduces user burden by avoiding repeated input requests especially in multi-step or long-form tasksSupports complex tasks like longitudinal workflows like in project planning, learning journeys by referencing or building on past progress.Memory used to access information can be ephemeral (short-term within a session) or persistent (long-term across sessions) and may include conversational context, behavioural signals, or explicit inputs.How to use this patternDefine the user context and choose memory typeChoose memory type like ephemeral or persistent or both based upon use case. A shopping assistant might track interactions in real time without needing to persist data for future sessions whereas personal assistants need long-term memory for personalization.Use memory intelligently in user interactionsBuild base prompts for LLM to recall and communicate information contextually (E.g., “Last time you preferred a lighter tone. Should I continue with that?”).Communicate transparency and provide controlsClearly communicate what’s being saved and let users view, edit or delete stored memory. Make “delete memories” an accessible action. E.g. ChatGPT offers extensive controls across it’s platform to view, update, or delete memories anytime.13. Provide contextual input parametersContextual Input parameters enhance the user experience by streamlining user interactions and gets to user goal faster. By leveraging user-specific data, user preferences or past interactions or even data from other users who have similar preferences, GenAI system can tailor inputs and functionalities to better meet user intent and decision making.How to use this patternLeverage prior interactions: Pre-fill inputs based on what the user has previously entered. Refer pattern 12, Memory and recall.Use auto complete or smart defaults: As users type, offer intelligent, real-time suggestions derived from personal and global usage patterns. E.g., Perplexity offers smart next query suggestions based on your current query thread.Suggest interactive UI widgets: Based upon system prediction, provide tailored input widgets like toasts, sliders, checkboxes to enhance user input. E.g., ElevenLabs allows users to fine-tune voice generation settings by surfacing presets or defaults.14. Design for co-pilot / co-editing / partial automationCo-pilot is an augmentation pattern where AI acts as a collaborative assistant, offering contextual and data-driven insights while the user remains in control. This design pattern is essential in domains like strategy, ideating, writing, designing or coding where outcomes are subjective, users have unique preferences or creative input from the user is critical.Co-pilot speed up workflows, enhance creativity and reduce cognitive load but the human retains authorship and final decision-making.How to use this patternEmbed inline assistance: Place AI suggestions contextually so users can easily accept, reject or modify them. E.g., Notion AI helps you draft, summarise and edit content while you control the final version.Save user intent and creative direction: Let users guide the AI with input like goals, tone, or examples, maintaining authorship and creative direction. E.g., Jasper AI allows users to set brand voice and tone guidelines, helping structure AI output to better match the user’s intent.15. Design user controls for automationBuild UI-level mechanisms that let users manage or override automation based upon user goals, context scenarios or system failure states.No system can anticipate all user contexts. Controls give users agency and keep trust intact even when the AI gets it wrong.How to use this patternUse progressive disclosure: Start with minimal automation and allow users to opt into more complex or autonomous features over time. E.g., Canva Magic Studio starts with simple AI suggestions like text or image generation then gradually reveals advanced tools like Magic Write, AI video scenes and brand voice customisation.Give users automation controls: UI controls like toggles, sliders, or rule-based settings to let users choose when and how automation can be controlled. E.g., Gmail lets users disable Smart Compose.Design for automation error recovery: Give users correction when AI fails (false positives/negatives). Add manual override, undo, or escalate options to human support. E.g., GitHub Copilot suggests code inline, but developers can easily reject, modify or undo suggestions when output is off.16. Design for user input error statesGenAI systems often rely on interpreting human input. When users provide ambiguous, incomplete or erroneous information, the AI may misunderstand their intent or produce low-quality outputs.Input errors often reflect a mismatch between user expectations and system understanding. Addressing these gracefully is essential to maintain trust and ensure smooth interaction.How to use this patternHandle typos with grace: Use spell-checking or fuzzy matching to auto-correct common input errors when confidence is high (e.g., >80%), and subtly surface corrections (“Showing results for…”).Ask clarifying questions: When input is too vague or has multiple interpretations, prompt the user to provide missing context. In Conversation Design, these types of errors occur when the intent is defined but the entity is not clear. Know more about entity and intent. E.g., ChatGPT when given low-context prompts like “What’s the capital?”, it asks follow-up questions rather than guessing.Support quick correction: Make it easy for users to edit or override your interpretation. E.g., ChatGPT displays an edit button beside submitted prompts, enabling users to revise their input17. Design for AI system error statesGenAI outputs are inherently probabilistic and subject to errors ranging from hallucinations and bias to contextual misalignments.Unlike traditional systems, GenAI error states are hard to predict. Designing for these states requires transparency, recovery mechanisms and user agency. A well-designed error state can help users understand AI system boundaries and regain control.A Confusion matrix helps analyse AI system errors and provides insight into how well the model is performing by showing the counts of - True positives (correctly identifying a positive case) - False positives (incorrectly identifying a positive case) - True negatives (correctly identifying a negative case)- False negatives (failing to identify a negative case)Scenarios of AI errors and failure statesSystem failure (wrong output)False positives or false negatives occur due to poor data, biases or model hallucinations. E.g., Citibank financial fraud system displays a message “Unusual transaction. Your card is blocked. If it was you, please verify your identity”System limitation errors (no output)True negatives occur due to untrained use cases or gaps in knowledge. E.g., when an ODQA system is given a user input outside the trained dataset, throws the following error “Sorry, we don’t have enough information. Please try a different query!”Contextual errors (misunderstood output)True positives that confuse users due to poor explanations or conflicts with user expectations comes under contextual errors. E.g., when user logs in from a new device, gets locked out. AI responds: “Your login attempt was flagged for suspicious activity”How to use this patternCommunicate AI errors for various scenarios: Use phrases like “This may not be accurate”, “This seems like…” or surface confidence levels to help calibrate trust.Use pattern convey model confidence for low confidence outputs.Offer error recovery: Incase of System failure or Contextual errors, provide clear paths to override, retry or escalate the issue. E.g., Use way forwards like “Try a different query,” or “Let me refine that.” or “Contact Support”.Enable user feedback: Make it easy to report hallucinations or incorrect outputs. Read more about pattern 19. Design to capture user feedback.18. Design to capture user feedbackReal-world alignment needs direct user feedback to improve the model and thus the product. As people interact with AI systems, their behaviours shape and influence the outputs they receive in the future. Thus, creating a continuous feedback loop where both the system and user behaviour adapt over time. E.g., ChatGPT uses Reaction buttons and Comment boxes to collect user feedback.How to use this patternAccount for implicit feedback: Capture user actions such as skips, dismissals, edits, or interaction frequency. These passive signals provide valuable behavioral cues that can tune recommendations or surface patterns of disinterest.Ask for explicit feedback: Collect direct user input through thumbs-up/down, NPS rating widgets or quick surveys after actions. Use this to improve both model behavior and product fit.Communicate how feedback is used: Let users know how their feedback shapes future experiences. This increases trust and encourages ongoing contribution.19. Design for model evaluationRobust GenAI models require continuous evaluation during training as well as post-deployment. Evaluation ensures the model performs as intended, identify errors and hallucinations and aligns with user goals especially in high-stakes domains.How to use this patternThere are three key evaluation methods to improve ML systems.LLM based evaluations (LLM-as-a-judge) A separate language model acts as an automated judge. It can grade responses, explain its reasoning and assign labels like helpful/harmful or correct/incorrect.E.g., Amazon Bedrock uses the LLM-as-a-Judge approach to evaluate AI model outputs.A separate trusted LLM, like Claude 3 or Amazon Titan, automatically reviews and rates responses based on helpfulness, accuracy, relevance, and safety. For instance, two AI-generated replies to the same prompt are compared, and the judge model selects the better one.This automation reduces evaluation costs by up to 98% and speeds up model selection without relying on slow, expensive human reviews.Enable code-based evaluations: For structured tasks, use test suites or known outputs to validate model performance, especially for data processing, generation, or retrieval.Capture human evaluation: Integrate real-time UI mechanisms for users to label outputs as helpful, harmful, incorrect, or unclear. Read more about it in pattern 19. Design to capture user feedbackA hybrid approach of LLM-as-a-judge and human evaluation drastically boost accuracy to 99%.20. Design for AI guardrailsDesign for AI guardrails means building practises and principles in GenAI models to minimise harm, misinformation, toxic behaviour and biases. It is a critical consideration toProtect users and children from harmful language, made-up facts, biases or false information.Build trust and adoption: When users know the system avoids hate speech and misinformation, they feel safer and show willingness to use it often.Ethical compliance: New rules like the EU AI act demand safe AI design. Teams must meet these standards to stay legal and socially responsible.How to use this patternAnalyse and guide user inputs: If a prompt could lead to unsafe or sensitive content, guide users towards safer interactions. E.g., when Miko robot comes across profanity, it answers“I am not allowed to entertain such language”Filter outputs and moderate content: Use real-time moderation to detect and filter potentially harmful AI outputs, blocking or reframing them before they’re shown to the user. E.g., show a note like: “This response was modified to follow our safety guidelines.Use pro-active warnings: Subtly notify users when they approach sensitive or high stakes information. E.g., “This is informational advice and not a substitute for medical guidance.”Create strong user feedback: Make it easy for users to report unsafe, biased or hallucinated outputs to directly improve the AI over time through active learning loops. E.g., Instagram provides in-app option for users to report harm, bias or misinformation.Cross-validate critical information: For high-stakes domains (like healthcare, law, finance), back up AI-generated outputs with trusted databases to catch hallucinations. Refer pattern 10, Provide data sources.21. Communicate data privacy and controlsThis pattern ensures GenAI applications clearly convey how user data is collected, stored, processed and protected.GenAI systems often rely on sensitive, contextual, or behavioral data. Mishandling this data can lead to user distrust, legal risk or unintended misuse. Clear communication around privacy safeguards helps users feel safe, respected and in control. E.g., Slack AI clearly communicates that customer data remains owned and controlled by the customer and is not used to train Slack’s or any third-party AI modelsHow to use this patternShow transparency: When a GenAI feature accesses user data, display explanation of what’s being accessed and why.Design opt-in and opt-out flows: Allow users to easily toggle data sharing preferences.Enable data review and deletion: Allow users to view, download or delete their data history giving them ongoing control.ConclusionThese GenAI UX patterns are a starting point and represent the outcome of months of research, shaped directly and indirectly with insights from notable designers, researchers, and technologists across leading tech companies and the broader AI communites across Medium and Linkedin. I have done my best to cite and acknowledge contributors along the way but I’m sure I’ve missed many. If you see something that should be credited or expanded, please reach out.Moreover, these patterns are meant to grow and evolve as we learn more about creating AI that’s trustworthy and puts people first. If you’re a designer, researcher, or builder working with AI, take these patterns, challenge them, remix them and contribute your own. Also, please let me know in comments about your suggestions. If you would like to collaborate with me to further refine this, please reach out to me.20+ GenAI UX patterns, examples and implementation tactics was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.