← 🤖 Retrieval & Agents
Core
Retrieval vs Long Context
When you can fit a whole document in a model's large context window, should you, or should you retrieve only the relevant chunks? Long context is simpler but expensive (quadratic attention), slower, and unevenly used (lost in the middle); retrieval is cheaper, faster, updates without retraining, and surfaces only what matters. The answer is usually retrieval for large, changing, or partially-relevant corpora, and long context for small, cohesive inputs. Applied-AI interviews probe it because 'just use the big context window' is a common, costly oversimplification.
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TAUGHT IN THESE COURSES
This concept is a reference. These lessons put it in sequence, with what comes before and after it.
Applied AI Engineering·How language models work, enough to build onFree13mA big context window does not mean you should fill itContext windows keep growing, and the instinct is to stop thinking about what goes in them. This lesson closes the module by showing why capacity is not recall, what actually degrades as the window fills, and how to decide between more context and better context.Applied AI Engineering·RetrievalFree13mWhy retrieval exists, and what it actually buys youRetrieval is the standard answer to making a model useful on knowledge it never saw. This lesson covers the three problems it solves at once, the one thing it does not fix, and the shape every system in this module is built from.Applied AI Engineering·RetrievalFree12mWhen retrieval is the wrong answerRetrieval has become the reflex, and it is wrong often enough to be worth a lesson. Four situations where something simpler or entirely different is the better system, and the decision procedure that gets you there in ten minutes.
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
RAG & Agent System DesignContext windows are now huge. When do you just stuff everything in context instead of building RAG?→RAG & Agent System DesignHow do you choose top-k and the context budget for RAG, given recall, noise, and cost all pull against each other?→RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→RAG & Agent System DesignWhat is query transformation in RAG (HyDE, decomposition, step-back), and when does each help?→RAG & Agent System DesignWhat is GraphRAG, and when does it beat traditional vector RAG?→
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