Blog · LLM Engineering
RAG, model selection, cost, evaluation, and the plumbing behind reliable LLM features.
Stop picking one winner. A three-tier model strategy (frontier, workhorse, self-hosted) with routing by task, data residency, and eval-driven switching.
Cost per task, model tiering, prompt caching, routing, batch processing, and hard caps: how to run LLM features cheaply without a quality regression.
Why RAG demos impress and production RAG fails, and the engineering that closes the gap: permission-aware retrieval, hybrid search, citations, and evals.
What the 2026-07-28 MCP spec changes mean operationally, what the adoption numbers say, and how to expose internal systems as MCP servers safely.
We build the AI agents, automation, and software behind ideas like these.