Every issue, chronological.

The model got deprecated. Here is the migration you should have built.

A shadow-eval gate that scores the candidate model before your traffic sees it.

The agent loop is a queue you did not design

The thirty-line agent runtime that keeps a tool-calling loop from becoming a bill.

Background jobs for AI workloads: the queue architecture you actually need

When to fine-tune, when to use RAG, and when to just fix the prompt

How to structure an AI engineering team

Who owns the eval set? Who owns the prompt? Who decides when to upgrade the embedding model?

The 90% discount on input tokens that most teams do not claim.

Streaming LLM responses without breaking your backend

Chunking strategies for production RAG

Hybrid search: combining BM25 with embeddings

Pure vector search misses exact-match queries.

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...

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.

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.

The vector DB benchmark lies about your workload. Here is the matrix that doesn't.

Your LLM call isn't slow. One of the four stages is.

You shipped RAG two weeks ago. The demo was 800ms, production is 4.2 seconds at p95, and the only thing your trace tells you is "LLM call: 3.8s". So you start guessing. Maybe the model is slow today.