Teams spend weeks tweaking system prompts while accuracy stays stuck. The real gains come from structuring context: feed the model the right data instead of better adjectives.

Article covers: - Context pipeline architecture (Query → Hydrator → Structured Context → Intelligence) - Three laws: structure beats prose, dynamic injection, information gain metric - Production examples: fintech chatbot latency improvements, SaaS cost optimizations - Failure modes I've actually hit: over-engineering, latency budgets exceeded, wrong context retrieval

Includes TypeScript code examples with error handling, caching, timeouts, and test patterns.