Context: the meaning above your data.

Why context is the first C

Every AI failure in a real enterprise looks the same at the surface — wrong answer, missed action, hallucinated metric — and traces back to the same root: the system did not know what this business actually means by customer, active, or net revenue. Models do not invent meaning. They borrow it from whatever you put in front of them. Without context, even a state-of-the-art model becomes a confident liar.

Context is the layer that fixes this. It is the place where columns become concepts, joins become relationships, and metrics carry a single definition that the whole company agrees on. It sits above the warehouse, above the lake, above the documents, and it is the only durable moat between a working AI system and a demo that breaks the moment a real user asks a real question.

What sits inside the context layer

• Semantic layer: business terms mapped to physical tables, with one canonical definition per metric.
• Ontology: the relationships between entities — customer, order, account, contract — that let an agent traverse meaning, not just rows.
• Business memory: the policies, decisions, and definitions a human would tell a new hire on day one. Captured, versioned, and queryable.
• Retrieval pipelines: not just vector search — retrieval grounded in entities, metrics, and policies so the model fetches the right thing.
• Evaluation harness: tests that catch when meaning drifts, before users do.

RAG is not context

Retrieval-augmented generation is a transport mechanism. It moves chunks of text into a prompt. Context is the meaning those chunks carry. A team that ships RAG without a semantic layer is a team that has built a faster way to be wrong. Treat RAG as one client of your context layer, not as the context layer itself.

How to start

Begin with the ten questions your business asks most often. For each one, write the metric, the entities, the filters, and the owner. That document is your seed semantic layer. Then connect one model and one agent through it. You will learn more in two weeks than in two quarters of pure platform work.

The book covers the full path — definitions, tooling, evaluation, and the role of the Context Engineer — across five chapters in Part 2.