Building Production RAG

Problem Framing and Dataset Honesty

Before you write a single line of RAG code, you need to be ruthlessly honest about what problem you're solving and whether your data can actually solve it.

Chunking Strategies That Actually Move Recall

The chunking decision you make in hour one will haunt your recall numbers for months — here's what the tradeoffs actually look like in production.

Embedding Choice and Dimensionality

The embedding model you pick shapes every downstream tradeoff in your RAG pipeline — here's how to choose without regretting it six months later.

Hybrid Search: BM25 + Vector

Pure vector search leaves precision on the table for exact-match queries — here's how to combine lexical and semantic retrieval without making your pipeline a mess.

Re-ranking Architectures

First-stage retrieval gets you candidates — re-ranking is what turns a decent recall number into an answer users actually trust.

Caching, Batching, and Cost Control

A RAG system that works but costs $40K/month to run isn't a product — here's the concrete cost math and the levers that actually move it.

Evaluation Harness from Scratch

A RAG pipeline without an evaluation harness is a system you can only improve by accident — here's how to build the infrastructure that makes intentional progress possible.

Observability for Retrieval

If your RAG system fails silently and you don't know until a user screenshots the bad answer, you don't have observability — here's what to actually instrument.

Multi-Tenant Isolation

Letting multiple customers share a RAG pipeline is an engineering win until one tenant's data leaks into another's answer — here's how to prevent that.

Failure Modes and Runbooks

Every RAG system that runs in production will fail in ways you didn't anticipate — here are the incidents that actually happen and how to resolve them at 2am.