Context

“Leave it by the bank before noon.”

Every word is familiar. Yet the instruction is incomplete. Which bank? Leave what? Whose noon? Who made the request—and do they have the authority to do so?

This gap between readable language and usable meaning is context.

Volume I, Token, followed language into the machine and showed how words become numerical representations that a model can process. But a token cannot determine its own meaning. It arrives surrounded by possibilities. Context: Drive the Known to Derive the Unknown begins at that boundary and asks: what selects the information that makes one interpretation appropriate now?

Following one deceptively simple instruction, the book travels through context windows, retrieval, embeddings, reranking, RAG, conversation state, persistent memory, knowledge graphs, MCP, provenance, security, and evaluation. It reveals that context is not merely a larger prompt or a pile of retrieved documents. It is an engineered chain of decisions: what enters, what is excluded, which source is trusted, what is transformed, and what may be lost.

Stories make each problem intuitive. Mechanisms reveal the system underneath. Derivations and laboratories make the ideas inspectable and reproducible. A recurring Canonical Context Trace connects the journey, showing how an ambiguous request becomes a bounded, authorized, and traceable context packet.

But context has a limit. It can find relevant evidence, preserve conflicting sources, and expose uncertainty. It cannot, by itself, determine what validly follows from that evidence. Relevance is not reasoning, and a citation is not a conclusion.

That unresolved boundary opens Volume III, Reasoning. If Token asks how language enters the machine, and Context asks what information should surround it, Reasoning asks the next unavoidable question:

Given what is known, what can the system justifiably conclude?