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Beyond the Prompt: What Google’s LLM Advice Doesn’t Quite Tell You
Latest Machine Learning
Beyond the Prompt: What Google’s LLM Advice Doesn’t Quite Tell You
0 like
May 18, 2025
Share this post
Author: Mayank Bohra
Originally published on Towards AI.
Image by the author
Alright, let’s talk about prompt engineering. Every other week, it seems there is a new set of secrets or magical techniques guaranteed to unlock AI perfection. Recently, a whitepaper from Google made the rounds, outlining their take on getting better results from Large Language Models.
Look, effective prompting is absolutely necessary. It’s the interface layer, how we communicate our intent to these incredibly powerful, yet often frustrating opaque, models. Think of it like giving instructions to a brilliant but slightly eccentric junior engineer who only understands natural language. You need to be clear, specific, and provide context.
But let’s be pragmatic. The idea that a few prompt tweaks will magically “10x” your results for every task is marketing hype, not engineering reality. These models, for all their capabilities, are fundamentally pattern-matching machines operating within a probabilistic space. They don’t understand in the way a human does. Prompting is about nudging that pattern matching closer to the desired outcome.
So, what did Google’s advice cover, and what’s the experience builder’s take on it? The techniques generally boil down to principles we’ve known for a while: clarity, structure, providing examples and iteration.
The Fundamentals: Clarity, Structure, Context
Much of the advice centers on making your intent unambiguous. This is ground zero for dealing with LLMs. They excel at finding patterns in vast amounts of data, but they stumble on vagueness.
Being Specific and Detailed: This isn’t a secret; it’s just good communication. If you ask for “information about AI”, you’ll get something generic. If you ask for “a summary of recent advancements in Generative AI model architecture published in research papers since April 2025, focusing on MoE models”, you give the model a much better target.
Defining Output Format: Models are flexible text generators. If you don’t specify structure, you’ll get whatever feels statistically probable based on the training data, which is often inconsistent. Telling the model “Respond in JSON format with keys ‘summary’ and ‘key_findings’” isn’t magic; it’s setting clear requirements.
Providing Context: Models have limited context windows. Showing your entire codebase or all user documentation in won’t work. You need to curate teh relevant information. This principle is the entire foundation of Retrieval Augmented Generation, where you retrieve relevant chunks of data and then provide them as context to the prompt. Prompting alone without relevant external knowledge only leverage the model’s internal training data, which might be outdated or insufficient for domain-specific tasks.
These points are foundational. They’re less about discovering hidden model behaviors and more about mitigating the inherent ambiguity of natural language and the model’s lack of true world understanding.
Structuring the Conversation: Roles and Delimiters
Assigning a roleor using delimitersare simple yet effective ways to guide the model’s behavior and separate instructions from input.
Assigning a Role: This is a trick to prime the model to generate text consistent with a certain persona or knowledge domain it learned during training. It leverage the fact that the model has seen countless examples of different writing styles and knowledge expressions. It works, but it’s a heuristic, not a guarantee of factual accuracy or perfect adherence to the role.
Using Delimiters: Essential for programmatic prompting. When you’re building an application that feeds user input into a prompt, you must use delimitersto clearly separated the user’s potentially malicious input from your system instructions. This is a critical security measure against prompt injection attacks, not just a formatting tip.
Nudging the Model’s Reasoning: Few-shot and Step-by-Step
Some techniques go beyond just structuring the input; they attempt to influence the model’s internal processing.
Few-shot Prompts: Providing a few examples of input/output pairsif often far more effective than just describing the task. Why? Because the model learns the desired mapping from the examples. It’s pattern recognition again. This is powerful for teaching specific formats or interpreting nuanced instructions that hard to describe purely verbally. It’s basically in-context learning.
Breaking Down Complex Tasks: Asking the model to think step-by-stepencourages it to show intermediate steps. This often leads to more accurate final results, especially for reasoning-heavy tasks. Why? It mimics hwo humans solve problems and forces the model to allocate computational steps sequentially. It’s less about a secret instruction and more about guiding the model through a multi-step process rather than expecting it to leap to the answer in one go.
The Engineering Angle: Testing and Iteration
The advice also includes testing and iteration. Again, this isn’t unique to prompt engineering. It’s fundamental to all software development.
Test and Iterate: You write a prompt, you test it with various inputs, you see where it fails or is suboptimal, you tweak the prompt, and you test again. This loop is the reality of building anything reliable with LLMs. It highlights that prompting is often empirical; you figure out what works by trying it. This is the opposite of a predictable, documented API.
The Hard Truth: Where Prompt Engineering Hits a Wall
Here’s where the pragmatic view really kicks in. Prompt engineering, while crucial, has significant limitations, especially for building robust, production-grade applications:
Context Window Limits: There’s only so much information you can cram into a prompt. Long documents, complex histories, or large datasets are out. This is why RAG systems are essential — they manage and retrieve relevant context dynamically. Prompting alone doesn’t solve the knowledge bottleneck.
Factual Accuracy and Hallucinations: No amount of prompting can guarantee a model won’t invent facts or confidently present misinformation. Prompting can sometimes mitigate this by, for examples, telling the model to stick only to the provided context, but it doesn’t fix the underlying issue that the model is a text predictor, not a truth engine.
Model Bias and Undesired Behavior: Prompts can influence output, but they can’t easily override biases embedded in the training data or prevent the model from generating harmful or inappropriate content in unexpected ways. Guardrails need to be implemented *outside* the prompt layer.
Complexity Ceiling: For truly complex, multi-step processes requiring external tool use, decision making, and dynamic state, pure prompting breaks down. This is the domain of AI agents, which use LLMs as the controller but rely on external memory, planning modules, and tool interaction to achieve goals. Prompting is just one part of the agent’s loop.
Maintainability: Try managing dozens or hundreds of complex, multi-line prompts across different features in a large application. Versioning them? Testing changes? This quickly becomes an engineering nightmare. Prompts are code, but often undocumented, untestable code living in strings.
Prompt Injection: As mentioned with delimiters, allowing external inputinto a prompt opens the door to prompt injection attacks, where malicious input hijacks the model’s instructions. Robust applications need sanitization and architectural safeguards beyond just a delimiter trick.
What no one tells you in the prompt “secrets” articles is that the difficulty scales non-linearly with the reliability and complexity required. Getting a cool demo output with a clever prompt is one thing. Building a feature that consistently works for thousands of users on diverse inputs while being secure and maintainable? That’s a whole different ballgame.
The Real “Secret”? It’s Just Good Engineering.
If there’s any “secret” to building effective applications with LLMs, it’s not a prompt string. It’s integrating the model into a well-architected system.
This involves:
Data Pipelines: Getting the right data to the model.
Orchestration Frameworks: Using tools like LangChain, LlamaIndex, or building custom workflows to sequence model calls, tool use, and data retrieval.
Evaluation: Developing robust methods to quantitatively measure the quality of LLM output beyond just eyeballing it. This is hard.
Guardrails: Implementing safety checks, moderation, and input validation *outside* the LLM call itself.
Fallback Mechanisms: What happens when the model gives a bad answer or fails? Your application needs graceful degradation.
Version Control and Testing: Treating prompts and the surrounding logic with the same rigor as any other production code.
Prompt engineering is a critical *skill*, part of the overall toolkit. It’s like knowing how to write effective SQL queries. Essential for database interaction, but it doesn’t mean you can build a scalable web application with just SQL. You need application code, infrastructure, frontend, etc.
Wrapping Up
So, Google’s whitepaper and similar resources offer valuable best practices for interacting with LLMs. They formalize common-sense approaches to communication and leverage observed model behaviors like few-shot learning and step-by-step processing. If you’re just starting out, or using LLMs for simple tasks, mastering these techniques will absolutely improve your results.
But if you’re a developer, an AI practitioner, or a technical founder looking to build robust, reliable applications powered by LLMs, understand this: prompt engineering is table stakes. It’s necessary, but far from sufficient. The real challenge, the actual “secrets” if you want to call them that, lie in the surrounding engineering — the data management, the orchestration, the evaluation, the guardrails, and the sheer hard work of building a system that accounts for the LLM’s inherent unpredictability and limitations.
Don’t get fixated on finding the perfect prompt string. Focus on building a resilient system around it. That’s where the real progress happens.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
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#beyond #prompt #what #googles #llm
Beyond the Prompt: What Google’s LLM Advice Doesn’t Quite Tell You
Latest Machine Learning
Beyond the Prompt: What Google’s LLM Advice Doesn’t Quite Tell You
0 like
May 18, 2025
Share this post
Author: Mayank Bohra
Originally published on Towards AI.
Image by the author
Alright, let’s talk about prompt engineering. Every other week, it seems there is a new set of secrets or magical techniques guaranteed to unlock AI perfection. Recently, a whitepaper from Google made the rounds, outlining their take on getting better results from Large Language Models.
Look, effective prompting is absolutely necessary. It’s the interface layer, how we communicate our intent to these incredibly powerful, yet often frustrating opaque, models. Think of it like giving instructions to a brilliant but slightly eccentric junior engineer who only understands natural language. You need to be clear, specific, and provide context.
But let’s be pragmatic. The idea that a few prompt tweaks will magically “10x” your results for every task is marketing hype, not engineering reality. These models, for all their capabilities, are fundamentally pattern-matching machines operating within a probabilistic space. They don’t understand in the way a human does. Prompting is about nudging that pattern matching closer to the desired outcome.
So, what did Google’s advice cover, and what’s the experience builder’s take on it? The techniques generally boil down to principles we’ve known for a while: clarity, structure, providing examples and iteration.
The Fundamentals: Clarity, Structure, Context
Much of the advice centers on making your intent unambiguous. This is ground zero for dealing with LLMs. They excel at finding patterns in vast amounts of data, but they stumble on vagueness.
Being Specific and Detailed: This isn’t a secret; it’s just good communication. If you ask for “information about AI”, you’ll get something generic. If you ask for “a summary of recent advancements in Generative AI model architecture published in research papers since April 2025, focusing on MoE models”, you give the model a much better target.
Defining Output Format: Models are flexible text generators. If you don’t specify structure, you’ll get whatever feels statistically probable based on the training data, which is often inconsistent. Telling the model “Respond in JSON format with keys ‘summary’ and ‘key_findings’” isn’t magic; it’s setting clear requirements.
Providing Context: Models have limited context windows. Showing your entire codebase or all user documentation in won’t work. You need to curate teh relevant information. This principle is the entire foundation of Retrieval Augmented Generation, where you retrieve relevant chunks of data and then provide them as context to the prompt. Prompting alone without relevant external knowledge only leverage the model’s internal training data, which might be outdated or insufficient for domain-specific tasks.
These points are foundational. They’re less about discovering hidden model behaviors and more about mitigating the inherent ambiguity of natural language and the model’s lack of true world understanding.
Structuring the Conversation: Roles and Delimiters
Assigning a roleor using delimitersare simple yet effective ways to guide the model’s behavior and separate instructions from input.
Assigning a Role: This is a trick to prime the model to generate text consistent with a certain persona or knowledge domain it learned during training. It leverage the fact that the model has seen countless examples of different writing styles and knowledge expressions. It works, but it’s a heuristic, not a guarantee of factual accuracy or perfect adherence to the role.
Using Delimiters: Essential for programmatic prompting. When you’re building an application that feeds user input into a prompt, you must use delimitersto clearly separated the user’s potentially malicious input from your system instructions. This is a critical security measure against prompt injection attacks, not just a formatting tip.
Nudging the Model’s Reasoning: Few-shot and Step-by-Step
Some techniques go beyond just structuring the input; they attempt to influence the model’s internal processing.
Few-shot Prompts: Providing a few examples of input/output pairsif often far more effective than just describing the task. Why? Because the model learns the desired mapping from the examples. It’s pattern recognition again. This is powerful for teaching specific formats or interpreting nuanced instructions that hard to describe purely verbally. It’s basically in-context learning.
Breaking Down Complex Tasks: Asking the model to think step-by-stepencourages it to show intermediate steps. This often leads to more accurate final results, especially for reasoning-heavy tasks. Why? It mimics hwo humans solve problems and forces the model to allocate computational steps sequentially. It’s less about a secret instruction and more about guiding the model through a multi-step process rather than expecting it to leap to the answer in one go.
The Engineering Angle: Testing and Iteration
The advice also includes testing and iteration. Again, this isn’t unique to prompt engineering. It’s fundamental to all software development.
Test and Iterate: You write a prompt, you test it with various inputs, you see where it fails or is suboptimal, you tweak the prompt, and you test again. This loop is the reality of building anything reliable with LLMs. It highlights that prompting is often empirical; you figure out what works by trying it. This is the opposite of a predictable, documented API.
The Hard Truth: Where Prompt Engineering Hits a Wall
Here’s where the pragmatic view really kicks in. Prompt engineering, while crucial, has significant limitations, especially for building robust, production-grade applications:
Context Window Limits: There’s only so much information you can cram into a prompt. Long documents, complex histories, or large datasets are out. This is why RAG systems are essential — they manage and retrieve relevant context dynamically. Prompting alone doesn’t solve the knowledge bottleneck.
Factual Accuracy and Hallucinations: No amount of prompting can guarantee a model won’t invent facts or confidently present misinformation. Prompting can sometimes mitigate this by, for examples, telling the model to stick only to the provided context, but it doesn’t fix the underlying issue that the model is a text predictor, not a truth engine.
Model Bias and Undesired Behavior: Prompts can influence output, but they can’t easily override biases embedded in the training data or prevent the model from generating harmful or inappropriate content in unexpected ways. Guardrails need to be implemented *outside* the prompt layer.
Complexity Ceiling: For truly complex, multi-step processes requiring external tool use, decision making, and dynamic state, pure prompting breaks down. This is the domain of AI agents, which use LLMs as the controller but rely on external memory, planning modules, and tool interaction to achieve goals. Prompting is just one part of the agent’s loop.
Maintainability: Try managing dozens or hundreds of complex, multi-line prompts across different features in a large application. Versioning them? Testing changes? This quickly becomes an engineering nightmare. Prompts are code, but often undocumented, untestable code living in strings.
Prompt Injection: As mentioned with delimiters, allowing external inputinto a prompt opens the door to prompt injection attacks, where malicious input hijacks the model’s instructions. Robust applications need sanitization and architectural safeguards beyond just a delimiter trick.
What no one tells you in the prompt “secrets” articles is that the difficulty scales non-linearly with the reliability and complexity required. Getting a cool demo output with a clever prompt is one thing. Building a feature that consistently works for thousands of users on diverse inputs while being secure and maintainable? That’s a whole different ballgame.
The Real “Secret”? It’s Just Good Engineering.
If there’s any “secret” to building effective applications with LLMs, it’s not a prompt string. It’s integrating the model into a well-architected system.
This involves:
Data Pipelines: Getting the right data to the model.
Orchestration Frameworks: Using tools like LangChain, LlamaIndex, or building custom workflows to sequence model calls, tool use, and data retrieval.
Evaluation: Developing robust methods to quantitatively measure the quality of LLM output beyond just eyeballing it. This is hard.
Guardrails: Implementing safety checks, moderation, and input validation *outside* the LLM call itself.
Fallback Mechanisms: What happens when the model gives a bad answer or fails? Your application needs graceful degradation.
Version Control and Testing: Treating prompts and the surrounding logic with the same rigor as any other production code.
Prompt engineering is a critical *skill*, part of the overall toolkit. It’s like knowing how to write effective SQL queries. Essential for database interaction, but it doesn’t mean you can build a scalable web application with just SQL. You need application code, infrastructure, frontend, etc.
Wrapping Up
So, Google’s whitepaper and similar resources offer valuable best practices for interacting with LLMs. They formalize common-sense approaches to communication and leverage observed model behaviors like few-shot learning and step-by-step processing. If you’re just starting out, or using LLMs for simple tasks, mastering these techniques will absolutely improve your results.
But if you’re a developer, an AI practitioner, or a technical founder looking to build robust, reliable applications powered by LLMs, understand this: prompt engineering is table stakes. It’s necessary, but far from sufficient. The real challenge, the actual “secrets” if you want to call them that, lie in the surrounding engineering — the data management, the orchestration, the evaluation, the guardrails, and the sheer hard work of building a system that accounts for the LLM’s inherent unpredictability and limitations.
Don’t get fixated on finding the perfect prompt string. Focus on building a resilient system around it. That’s where the real progress happens.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI - Medium
Share this post
#beyond #prompt #what #googles #llm
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