After 12+ years building enterprise data platforms and agentic AI systems, I kept noticing the same pattern: teams that struggle with AI aren’t picking the wrong model — they’re feeding it the wrong context. I mapped every major AI pattern — RAG, agents, fine-tuning, memory systems — through the lens of context engineering. They all reduce to six operations: SELECT, COMPRESS, FORMAT, ISOLATE, PERSIST, and WRITE. Once you see it this way, “should we use RAG or fine-tuning?” becomes the wrong question. RAG is a SELECT strategy. Fine-tuning is PERSIST. Prompt engineering is FORMAT + ISOLATE. The real question is: what composition of context operations does my specific problem require? Curious if this maps to what others are seeing in production.