Building Production RAG Systems: Lessons from the Field

Building a RAG system that works in a demo is straightforward. Building one that works reliably in production is a different challenge entirely. After deploying several RAG systems for clients, I've learned that the gap between prototype and production often comes down to details that aren't covered in tutorials.

The Chunking Problem

Most RAG tutorials suggest simple approaches like splitting documents every 500 tokens. In practice, this leads to poor retrieval because semantic boundaries rarely align with token counts.

What works better:

• Semantic chunking: Split on paragraph or section boundaries
• Overlap: Use 10-20% overlap between chunks to preserve context
• Metadata preservation: Keep document structure information with each chunk

The key insight is that your chunking strategy should match how users will query the system. If users ask questions about specific sections, your chunks should align with those sections.

Embedding Selection Matters

The choice of embedding model significantly impacts retrieval quality. While OpenAI's text-embedding-3-small is a solid default, specialized models often perform better for domain-specific content.

Consider these factors:

1. Dimensionality vs cost: Higher dimensions capture more nuance but increase storage and compute
2. Domain fit: Legal, medical, and technical domains benefit from fine-tuned embeddings
3. Multilingual needs: Some models handle multiple languages better than others

Retrieval Isn't Just Vector Search

Pure vector similarity search has limitations. Hybrid approaches that combine semantic search with keyword matching often outperform either method alone.

A practical hybrid setup:

# Combine BM25 and vector search
def hybrid_search(query, k=5):
    vector_results = vector_store.similarity_search(query, k=k*2)
    keyword_results = bm25_search(query, k=k*2)
    return reciprocal_rank_fusion(vector_results, keyword_results, k=k)

Evaluation is Non-Negotiable

You can't improve what you don't measure. Before deploying, establish baseline metrics:

• Retrieval precision: Are the retrieved chunks relevant?
• Answer accuracy: Is the generated response correct?
• Latency: Is the response time acceptable?

Build a test set of queries with known good answers and run evaluations on every change.

Production Considerations

Beyond the core RAG pipeline, production systems need:

• Caching: Cache embeddings for frequently accessed documents
• Rate limiting: Protect against abuse and control costs
• Monitoring: Track retrieval quality metrics over time
• Fallbacks: Handle cases where retrieval confidence is low

Conclusion

Production RAG systems require attention to details that prototypes can ignore. Invest time in proper chunking, evaluate different embedding models, use hybrid retrieval, and build robust evaluation pipelines. The extra effort pays off in reliability and user satisfaction.