Building Production-Grade RAG Systems

Retrieval-Augmented Generation (RAG) is becoming the standard for grounding LLMs on private data. But building a prototype is easy; building a production system is hard.

The Retrieval Challenge

Simple cosine similarity often fails for complex queries.

• Hybrid Search: Combining keyword search (BM25) with vector search often yields better results.
• Re-ranking: Using a cross-encoder model to re-rank the top K retrieved documents can significantly improve relevance.

Chunking Strategies

Fixed-size chunking is a good starting point, but semantic chunking or recursive retrieval (parent-child chunking) preserves context better.

Evaluation

How do you know if your RAG system is working? We use frameworks like Ragas and TruLens to measure:

• Faithfulness: Is the answer derived from the context?
• Answer Relevance: Does the answer address the query?

Moving from a demo to production requires robust evaluation pipelines and continuous monitoring.