Production RAG Architecture: Scaling Retrieval Systems

From It works on my laptop to It works for 10,000 users.

The Prototype Stack (Do Not Deploy This)

Vector DB: In-memory FAISS.
Ingestion: Script that runs once on your desktop.
Search: Simple Cosine Similarity.
Response: Direct pass to GPT-4.

This falls apart the moment data changes or users scale.

The Production Stack 🏗️

1. The Ingestion Pipeline (ETL)

You need a continuous pipeline (using Airflow, Prefect, or Temporal) that watches your source data.

Change Data Capture (CDC): If a row changes in SQL, update the vector.
Versioning: Keep track of which embedding model version was used. You cant compare v1 vectors with v2 vectors.

2. Hybrid Search (The Standard)

We rarely use pure vector search anymore. We use Hybrid Search:

Sparse Vector (BM25): Good for keywords (Acme Corp, Error 503).
Dense Vector (Embeddings): Good for concepts (How do I reset my password?).
Reciprocal Rank Fusion (RRF): An algorithm to combine the two lists of results into one best-of-both-worlds list.

3. The Re-Ranking Step 🥈

This is the secret sauce of high-quality RAG.

Retrieve 50 results using Hybrid Search (fast, cheap).
Pass those 50 results to a Cross-Encoder Re-ranker (like Cohere Rerank or BGE-Reranker). This is a slower, smarter model that reads every pair and scores relevance.
Take the top 5 from the Re-ranker and send to the LLM.

Result: Massive accuracy jump.

4. Query Rewriting

Users write bad queries. policy for that thing
We use a cheap LLM step before the search to rewrite the query: What is the company policy for expense reimbursement?

Conclusion

Production RAG is an orchestration problem, not just an AI problem. It requires database skills, latency engineering, and rigorous evaluation.

If your RAG bot is hallucinating, dont just blame the prompt. Look at the architecture.

This concludes our RAG Done Right series. Need help architecting your system? [Contact our Engineering Team](#).

How This Article Was Made

This article is a live example of the AI-enabled content workflow we build for clients.

Stage	Who	What
Research	Claude Opus 4.5	Analyzed current industry data, studies, and expert sources
Curation	Tom Hundley	Directed focus, validated relevance, ensured strategic alignment
Drafting	Claude Opus 4.5	Synthesized research into structured narrative
Fact-Check	Human + AI	All statistics linked to original sources below
Editorial	Tom Hundley	Final review for accuracy, tone, and value

The result: Research-backed content in a fraction of the time, with full transparency and human accountability.

Why We Work This Way

Were an AI enablement company. It would be strange if we didnt use AI to create content. But more importantly, we believe the future of professional content isnt AI vs. Human—its AI amplifying human expertise.

Every article we publish demonstrates the same workflow we help clients implement: AI handles the heavy lifting of research and drafting, humans provide direction, judgment, and accountability.

Want to build this capability for your team? Lets talk about AI enablement →