5 Context-Engineering Principles for Production AI

What this post added

This post introduces five principles for production AI context engineering: 1. Treat the whole run as the cost unit, not the turn, highlighting semantic caching (Redis LangCache) for cost savings. 2. Filter tool outputs at ingestion to prevent bloat and context poisoning. 3. Keep static and dynamic context in separate layers, leveraging unified data layers like Redis Iris for coherence. 4. Treat retrieval as a budget decision, not a fetch-everything default, discussing the 'lost in the middle' problem and the role of re-ranking. 5. Acknowledge that context failures can be invisible to standard evaluations. It also mentions Redis's capabilities in vector search (FLAT, HNSW, SVS-VAMANA indexes) and hybrid retrieval.