Context Engineering: What Every PM Building AI Needs to Know
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Context Engineering: What Every PM Building AI Needs to Know
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The best prompt engineer I know told me he stopped writing prompts.
He said: "Prompts are maybe 5% of what makes AI actually useful. The other 95%? It's everything the model sees before you even ask a question."
If you're building AI features and still obsessing over prompt wording, you're optimizing the wrong thing.
In this episode, I break down context engineering—what it is, where the term comes from, and how product managers can own the context window without writing code.
**What you'll learn:**
- Why "know your user" is the foundation of context engineering
- The 3 types of retrieval: keyword, semantic, and graph RAG
- Why more context actually hurts performance (context rot)
- How to build evals that learn from future outcomes
- 5 actionable homework items you can start today
**People mentioned:**
- Simon Willison (AI Engineer, Creator of Datasette)
- Kevin Weil (CPO at OpenAI)
**Key terms:**
- Context window
- RAG (Retrieval Augmented Generation)
- Semantic search / Vector databases
- Graph RAG / Knowledge graphs
- Context rot
- Evals / Data flywheel
Context engineering is where product strategy meets model behavior. The best AI products aren't using better models—they're using better context.
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