Building impactful AI products for design and product leaders, Part 3: Understand AI architectures: RAG, Agents, Oh My!

Key Insights

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  Large language models are stateless and rely entirely on the provided context window for each response.

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  The context window is essentially a text file aggregating system instructions, user queries, documents, and other relevant data.

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  All modern AI techniques (retrieval, augmented generation, tool use) aim to improve the quality and relevance of this context window.

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  Tool use allows the model to autonomously decide when to invoke external APIs or services based on the input query.

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  Agent models enhance tool use by planning and executing multiple tool calls in an iterative, reasoning loop until a task is complete.

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  Post-training with billions of examples significantly improves models' abilities in reasoning, tool use, and planning.

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  Designing AI products should begin with user needs and the necessary context rather than starting with complex agent architectures.

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  Prompt structure, including semantic content and organization (e.g., XML tags), helps the model parse context effectively but is flexible.

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  Token limits constrain the context window size; modern models like Google’s can handle up to a million tokens, enabling very large context inputs.

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  User-specific data (e.g., PTO policy, employee info) can be integrated into the context window dynamically through backend queries to provide accurate personalized responses.

Notable Quotes