Your LLM feature works in a demo. I make it survive real traffic.

I gave my kids' book agent proper logging in a morning — three plain files — and spent the afternoon fixing what the logs immediately caught: 73% of GPU time wasted, books in the wrong language, and two ways the model broke its own tool calls. Real numbers, same-day payback.

I RL-tuned a 3B model on a single GPU to tie Claude Opus 4.8 on text-to-SQL, then went query by query to show why an equal score doesn't mean an equal model.

The enterprise-knowledge-search question is really build-vs-buy: pay for Glean, self-host the open-source Onyx, or build RAG on the Postgres you already run. Instead of a feature table, I stood up the full Onyx platform AND wrote the entire Postgres alternative in ~80 lines, pointed both at the same local Qwen 27B over the same company knowledge base, and asked the same questions. Both gave accurate, cited answers. The difference isn't quality — it's eleven containers and a connector marketplace versus one container and a prompt you own. Here's how to choose.

A semantic cache on pgvector deletes the LLM calls you're paying for twice — but only if you tune it. I replayed 6,000 real questions through a warming Postgres cache and measured the hit rate, the dollars, and the part nobody benchmarks: how often a 'close enough' match serves a confidently wrong answer.

A practical guide to proving your LLM feature actually works — tracing, offline evals, and online monitoring — with an honest tour of the mid-2026 tooling landscape: Langfuse, Opik, Phoenix, DeepEval, Ragas, promptfoo, and where Trigger.dev fits.