Hybrid search: combining BM25 with embeddings
Pure vector search misses exact-match queries.
Free with subscription · 25 entries
The production RAG eval cheatsheet.
25 failure modes to test before you ship. The symptom, the test, the snippet, the fix — for each one.
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What you actually get
01 / Production-grade
Tactics from real shipping, not blog posts about blog posts.
Every issue starts with a real failure mode, debug story, or architectural decision — drawn from systems serving actual users. If it works in a demo, it doesn't ship here.
02 / Backend-first
Written for engineers who ship the systems behind the demo.
APIs, queues, retrieval, caching, observability, evals. The boring infrastructure that makes LLM products actually work in production — not prompt engineering hot takes.
03 / One per week
A single deep dive every Tuesday. No filler, no daily noise.
Long enough to be useful, short enough to read on the commute. Roughly 1,800 words, one diagram, one runnable snippet per issue. That's the contract.
Recent issues
#006
Jun 23, 2026
Jun 23, 2026
#005
Jun 16, 2026
Jun 16, 2026
Reranking 101: the 50ms layer that decides whether your RAG works.
Your retriever gives you 50 results. Reranking turns the top 5 useful. The difference between a RAG system that answers customer questions and one that hallucinates with confidence is rarely the...
#004
Jun 9, 2026
Jun 9, 2026
Your LLM app fails with 200 OK. These are the three signals worth paging on.
A logging schema, three alert thresholds, and the shadow eval that catches drift before users do.
#003
Jun 2, 2026
Jun 2, 2026
Build the evals before the product. Or watch it regress in silence.
If your team cannot agree on what "good" means, you do not have a product. You have a demo.
#002
May 26, 2026
May 26, 2026
The vector DB benchmark lies about your workload. Here is the matrix that doesn't.
Production AI engineering. One Tuesday at a time.
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