Context Engineering: What Every PM Building AI Needs to Know

Context Engineering: What Every PM Building AI Needs to Know

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.

Why Karpathy Saying “I’m Behind” Should Matter to Every PM

🎧 Episode 001 — If Karpathy Feels Behind, What Does That Mean for Product Managers? Summary: In this debut episode of Contextually Aware, product manager Luis Calderon dives into a striking moment from the AI world. Andrej Karpathy — former Tesla AI lead, co-founder of OpenAI, and one of the most respected voices in AI — recently said he has “never felt this much behind as a programmer,” because artificial intelligence is fundamentally reshaping how software gets built. That change isn’t incremental — Karpathy describes the profession as being dramatically refactored by AI tools and paradigms.   In this episode, we turn that insight into meaning for product managers. What does feeling behind actually signal about how AI is changing software development? And what questions should PMs be asking about strategy, workflows, uncertainty, and success when AI becomes a core part of how products are built? Key Topics Covered: * A clear explanation of Karpathy’s candid reflection on AI and programming.   * Why this matters to product managers — beyond engineering hype. * How AI tools and abstractions (agents, workflows, memory layers) are changing mental models for building software. * Practical product strategy implications: decision-making, risk, and prioritization. * A big open question to carry forward: How do we define success when the tools we use are probabilistic and evolving? Who This Episode Is For: Product managers, technical leaders, builders, and anyone trying to make sense of how AI is changing the craft of building products — and who wants to keep up without drowning in hype. Resources Mentioned: * Karpathy’s reflective post on AI and programming being refactored by AI.   * Broader context on how software development paradigms are shifting (Software 3.0).   Connect & Engage: If this resonated, share it with a colleague and join the conversation! Tweet at @ContextuallyAware or connect on LinkedIn — your thoughts will shape future episodes.