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Finding and summarizing information may not sound like the sexiest task for generative AI until you need an article you posted on social media but cant remember the exact word or phrase you used, or you want to answer a quick how-to question without plowing through a lengthy software manual.There are a lot of ways to set up large language models (LLM) to answer queries based exclusively on information you give them. One of the easiest, which involves no coding at all, is to use a service like Googles NotebookLM or ChatGPT Projects.Below Ill take a look at four genAI web platforms, the strengths of each, and how they perform on sample tasks like searching through a software manual.4 sample tasksI tested several platforms on four different types of questions:Querying software documentationSearching my own LinkedIn postsFinding a variable ID for a specific topicGetting information about professional conferences4 generative AI platformsThere are an increasing number of options for no-code chat with your data. I looked at four of the best known and most popular: Googles NotebookLM, OpenAIs ChatGPT Projects, Anthropics Claude Projects, and Perplexity Spaces.NotebookLM is a dedicated app; the other platforms are more general chatbots that allow users to group and save chats, additional files, and custom instructions related to topics of their choice. These groups are called projects or spaces, depending on the service.Google NotebookLMNotebookLM has several advantages:Theres a free version.You dont have to write special prompts for it to search specifically through the info you upload.It returns citations along with its answers by default, so you can see the source text excerpts it used for its responses.You can give it URLs to read as source material.NotebookLM is probably best known for generating realistic audio podcasts from your notes, but its also quite good at answering questions.Workflow: Upload your content into a notebook and start asking questions.Users with free accounts can upload files of up to 500,000 words per source and 50 sources per notebook, or 200MB total for local uploads. And free accounts can have up to 100 notebooks and ask 50 questions per day. Paid Plus users get 500 notebooks, 300 sources per notebook, and 500 queries per day.Privacy: Google says it wont use your uploaded files or chat to train its models. Enterprise NotebookLM users via Google Cloud also wont have their feedback reviewed by human reviewers. Regardless of any privacy policies, though, know your employers AI policies for any work-related items.Sharing: Free users can share full notebooks, which include complete source documents, with specific users. Paid users can share either full notebooks or chat-only notebooks with specific users. Number of allowable shares depends on your account level.How it performed: Tied for the top spot at 4.5 out of 5.OpenAI ChatGPT ProjectsChatGPT Projects is available to all paid subscribers Plus, Enterprise, and Pro. OpenAI says projects are good for ongoing work, or just to keep things tidy.Until recently, projects could only use the GPT-4o LLM; but lately o3-mini and o3-mini-high have shown up as options. Free users without access to Projects can upload files to regular chats and bookmark those to get a somewhat similar experience.Unlike NotebookLM, ChatGPT probably wont give you links to see the original text cited in its answer unless you give it specific instructions to do so. (And even then, it might not.) In return, though, its answers will likely be more nicely formatted and arranged.Workflow: Create a new project by finding Projects in the left nav (youll only see this if youre a subscriber), hovering over it, and clicking the + sign. Give your project a name, and youll see a chat interface along with options to add files and custom instructions.There is a limit to the number of files that can be uploaded, according to ChatGPT help files, but that limit isnt specified.Privacy: You can opt out of having your data used to train OpenAI models in account Settings > Data Controls.Sharing: Projects arent shareable. Use Custom GPTs, which can use instructions, extra knowledge, and any combination of skills, instead. Those require paid accounts and can be made public or shared to anyone with the link.How it performed: Tied for the top spot at 4.5 out of 5.Anthropic Claude ProjectsIm a big Claude fan for a lot of use cases, as I like its writing style, its ability to write R code, and how it follows instructions. But Claude Projects, available only to paid subscribers, has some drawbacks for cases like this.Anthropic says, Each project includes a 200K context window, the equivalent of a 500-page book. That sounds like a lot, but Claude projects have a smaller capacity than the other options I tested. And the answers can degrade if you push up against the limits. Claude Projects is only available for paid accounts.If you program and use GitHub, Claude can connect to your GitHub account, making it easy to pull in coding or documentation files, which can be very handy. However, if you are interested in using online information in your queries, Claude Projects cant yet access the internet beyond GitHub or your own Google Docs.Alexey Shabanov at Testing Catalog reports that Anthropic is testing a feature called Harmony, which would let Claude access a local directory of files allowing the AI to read, index, and analyze content within the directory. How that might expand context for project queries isnt clear.Workflow: Create a new project by clicking on Projects toward the top of the left navigation or going straight to claude.ai/projects and then the Create Project button at the top right. Name the project, describe its purpose, click Create, and youll get a conventional chat interface on the left and an area to add project instructions and files on the right.Privacy: Anthropic says its default is to not use your chats and data to train its models.Sharing: Theres no sharing unless youre a Claude Teams subscriber.How it performed: 3 out of 5, largely because it got a 0 for one test when my data exceeded its project storage limit.Im not sure I would pay $20/month just for projects here, but I subscribe for other reasons and appreciate having them.Perplexity SpacesPerplexity also has a free version, which excels at targeted searches of information thats already on the web, such as software or hardware documentation. You can give Perplexity a domain, for example https://help.vivaldi.com/desktop/, and it will search all the content there. (NotebookLM also accepts URLs but for individual pages, not a domain.) This is extremely useful when online software documentation is scattered across many small files on the web.You need a paid subscription to upload your own files to Spaces and to use top-tier LLMs. If you dont want to pay, you can upload files to a regular Perplexity chat (maximum of 10 per day).Workflow: Create a new space by clicking on Spaces in the left nav or by going directly to perplexity.ai/spaces and clicking the Create a Space box. A dialog box pops up asking for a Space title, optional description, and optional custom instructions. Theres a chat interface on the left and context section on the right that includes your custom instructions, a files upload section, and a links upload section.Privacy: You can opt out of having your data used to train Perplexitys own models in your profile settings.Sharing: Paid users can have up to 5 collaborators; Enterprise Pro, unlimited.How it performed: 2.5 out of 5. To be fair, though, my tests didnt look at Perplexitys major strength: web search. If you want to combine you own data with web searching, Perplexity would do much better.During testing, it also felt like Perplexity was least able to figure out what I wanted, at least when using its default Auto LLM. Its always good to be as specific as possible with LLM queries, but I found this to be especially true with Perplexity.Tests and resultsHeres a summary of how the tools performed in my tests. Read on for details.TaskNotebookLMChatGPT ProjectsClaude ProjectsPerplexity SpacesSimple tech docs search1110.5Vague social media query10.510Variable ID lookup1101Find a conference0.510.51Find conference sessions110.50Ranking4.54.532.5 1 = correct response, 0.5 = partially correct, 0 = incorrect or no responseTest 1: Simple tech documentation searchQuestion: Whats the easiest way to get rid of extra white space in text?Info source: Documentation for the stringr package in the R programming. Stringr includes a handy str_squish() function to delete excess white space.Results: Claude, NotebookLM, and ChatGPT answered with str_squish(), which I consider the correct answer. Perplexity assumed I only cared about space at the beginning and end of the text and not in the middle. After a follow-up question, it also found the best function.Test 2: Somewhat vague search of my social media postsThis was a more difficult task, but something similar to what people might want help with in the real world.Question: I really liked an article about LLMs written by Lucas Mearian at Computerworld. Please tell me the specifics based on my LinkedIn posts that I uploaded.Info source: 2 years of my LinkedIn posts.Results: NotebookLM and Claude nailed their responses, each offering two options including the one I wanted. ChatGPT gave me somewhat related articles, but not the one I wanted. (Id been looking for What are LLMs, and how are they used in generative AI?)Perplexity with its default Auto LLM didnt give me anything useful, claiming there is no specific mention of an article about Large Language Models (LLMs) written by Lucas Mearian.Test 3: Find a US Census table ID for a specific topicA lot of businesses use the US Census Bureaus American Community Survey (ACS) for demographic information. With thousands of available data variables, it can be hard to find one that has the information you want. This type of query could represent a lot of other data lookups businesses might want to do with their own data.Question: What is the best variable to use to find information about the percent of workers who work from home?Info source: I downloaded and filtered several listings of ACS table variable IDs (filtered because a couple of the lists were too large), along with a general explanation of ACS tables from the Census Bureau website. Since some of these platforms dont accept CSV files in projects, I saved the variable data as tab-delimited .txt files.Expected response: Kyle Walker, director of the Center for Urban Studies at Texas Christian University and author of the tidycensus R package, used the DP03_0024P variable in one of his examples, so thats what I was expecting in a correct answer.Results: NotebookLM, ChatGPT, and Perplexity all gave me results I could use. (Unexpectedly, I learned that there is more than one correct answer ChatGPT and Perplexity both found other variables that include the percent of people working from home.)Claude couldnt compete on this one, since my three .txt files with data totaling less than 800KB exceeded its project knowledge limit.Test 4: Ask about professional conferencesThis test featured two questions for two different data sources: Ask about a conference that might fit my needs, and then ask about conference sessions at one specific conference.Question 1: Im looking for IDG events that will talk about artificial intelligence. Id like them to be within a 2-hour flight or so from Boston.Info source: The IDG global events calendar PDF.Expected result: The most complete correct answer would cite FutureIT New York in July and FutureIT Toronto April 30 May 1. Work+ in Nashville at a 2:50 flight would also be a reasonable suggestion.Results: ChatGPT nailed it with both its more advanced o3-mini-high model and its general 4o LLM, returning the two events that exactly match the criteria.Perplexitys Sonar LLM returned both events as well as the CIO100 conference in Arizona, although acknowledging that one is beyond a 2-hour flight.NotebookLM got it partially right, suggesting FutureIT New York and Work+ in Nashville (which it accurately said was reasonably close to Boston true, its less than 3 hours away). However, it missed Toronto.Claude with its older Sonnet 3.5 model returned both matching events, along with UK Events for reference, though outside your travel range but did not include Nashville. Claude with its newer Sonnet 3.7 in its default setting was worse, finding only one that matched, a couple of others in the US, and two in Europe (noting that those were outside the travel range). When I changed Sonnet 3.7 from its default to extended reasoning, it gave a better response: both the New York and Toronto events as well as a virtual event.Question 2: Tell me all the sessions at the NICAR conference for people who are already proficient in spreadsheets that is, they are not beginners, but they want to improve their spreadsheet skills.Data source: Text file of the full NICAR data journalism conference schedule.Results: NotebookLM gave me more than a dozen interesting suggestions involving Google Sheets, Excel, and Airtable, with only one that might not have been relevant. It was definitely more than I would have found by simply searching the conference web page for Excel and Sheets. Plus, because I could click to zoom into the exact schedule text it cited, it was easy to check for hallucinations.Brainstorming is one area where many experts say LLMs can shine. I plan to upload other conference schedules to NotebookLM in the future to make sure I dont overlook potentially useful sessions.ChatGPT also came up with 12+ sessions that could be of interest, arranged by date and time and more nicely formatted. Claude proposed slightly fewer, but all seemed to match.Perplexity was disappointing, claiming: While the provided information does not explicitly list sessions for those proficient in spreadsheets, several sessions at the NICAR 2025 conference could be beneficial for improving spreadsheet skills or learning advanced data analysis techniques. It suggested only three.RecommendationsGenerative AI cloud services can be a helpful, no-code way to answer questions about your own information both finding info you know exists and helping you discover new insights.If you want a platform thats easy, free, and cites sources so you can check for hallucinations, Googles NotebookLM is an excellent choice.If you already subscribe to ChatGPT, its projects are worth a test. Theyre set up to handle a wider range of requests than simply Q&A, and ChatGPTs responses are often better formatted and easier to read than NotebookLMs. If youre a free user, you can upload files to conventional ChatGPT chats and get similar capabilities.Claude may be a good option if you dont have large amounts of data per project and youre already subscribing, especially if you want it to answer questions about data in a GitHub repository. If one response is unsatisfactory, try changing model settings.I found Perplexity to be more compelling for answering questions about information on the web, especially for use cases like software help where the info is spread over a lot of different files within a domain such as slack.com/help. However, Id probably go with NotebookLM or ChatGPT for local data.