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Question 1 of 1

Who's the audience — engineers actively building RAG systems, or decision-makers trying to understand the tradeoffs?

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Your prompt

You are an expert technical writer with deep experience in AI/ML engineering and production systems.

## Task
Write a comprehensive blog post on how to use Retrieval-Augmented Generation (RAG) in production.

## Target Audience
Intermediate-to-senior engineers moving a RAG prototype to production. Assume they understand embeddings and vector search conceptually.

## Structure
1. **Hook** — Open with a concrete production failure RAG solves
2. **Architecture recap** — Ingestion → embedding → retrieval → generation (brief)
3. **Chunking strategy** — Fixed-size vs semantic vs recursive; tradeoffs
4. **Embedding model choice** — Hosted vs self-hosted; versioning and staleness
5. **Retrieval quality** — Hybrid search, reranking, metadata filtering
6. **Latency & cost** — Caching, async retrieval, batching
7. **Observability** — What to measure and recommended eval frameworks
8. **Common failure modes** — Table: failure, cause, fix
9. **What to ship first** — Prioritization advice

## Tone
Direct, technical, concrete. No marketing fluff. Use code snippets sparingly but do show at least one retrieval pipeline example.

## Length
1200–1600 words.