How to Build a Production RAG Pipeline: Senior Engineer Guide [2026]

By Citadel Cloud Management · July 2, 2026

Production RAG Pipeline: What Demos Don't Teach You

Retrieval-Augmented Generation works in demos. Production RAG requires decisions about chunking, embedding models, vector databases, retrieval quality, hallucination mitigation, and cost control.

Architecture

1. Ingestion — document parsing, chunking, embedding

2. Storage — vector database (Pinecone, Qdrant, pgvector)

3. Retrieval — similarity search + reranking

4. Generation — LLM with context window management

5. Guardrails — hallucination detection, citation grounding

Key Decisions

• Chunking: 512 tokens with 50-token overlap for most documents

• Embedding: OpenAI text-embedding-3-small for cost, BGE-large for self-hosted

• Vector DB: pgvector if you already use PostgreSQL, Qdrant for scale

• Reranker: Cohere Rerank or cross-encoder for precision

FAQ

Cost at scale?

~$0.10-$0.50 per query at 1M docs. Embedding is the biggest cost.

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