The canonical applied-LLM reference. The seven patterns sit on two axes — data ↔ user and defensive ↔ offensive — which together cover the whole surface of LLM products, not just agentic coding. We use the 2×2 to audit our own coverage: own deeply, touch lightly, or explicitly leave out (with reason).
Why fine-tuning is out of scope for us. In agentic coding, frontier off-the-shelf models with a good harness consistently outperform fine-tuned smaller models for the same dollar. Fine-tuning belongs to narrow specialized LLM products (classifiers, content moderation, voice agents). Recommend it only if a client has a non-agent LLM product and asks.
Eugene Yan · "Patterns for Building LLM-based Systems & Products"
Three uses. (1) Executive framing. Force the client to name whether they want defensive value (cost, safety, reliability) or offensive value (capability, knowledge). (2) Coverage audit. Place each proposed initiative on the matrix; gaps and overlaps surface immediately. (3) Reading list. Yan's article is the densest applied-LLM primer in the field — recommend it to a client's platform lead on day one.