Building RAG Systems for Production: Lessons Learned

Retrieval-Augmented Generation (RAG) has become the standard approach for building AI systems that need to provide accurate, up-to-date information. After deploying RAG systems for multiple enterprise clients, we’ve gathered key lessons that can help you avoid common pitfalls.

Why RAG Matters

The RAG Pipeline

flowchart LR
    subgraph Ingestion["Document Ingestion"]
        A[Documents] --> B[Chunking]
        B --> C[Embedding]
        C --> D[Vector DB]
    end

    subgraph Retrieval["Query Processing"]
        E[User Query] --> F[Query Embedding]
        F --> G[Vector Search]
        G --> H[Reranking]
    end

    subgraph Generation["Response Generation"]
        H --> I[Context Assembly]
        I --> J[LLM Prompt]
        J --> K[Response]
        K --> L[Citations]
    end

    D -.-> G

    style Ingestion fill:#e0f2fe,stroke:#0284c7
    style Retrieval fill:#fef3c7,stroke:#d97706
    style Generation fill:#dcfce7,stroke:#16a34a

    

Each component presents its own challenges and optimization opportunities.

Lesson 1: Chunking Strategy Matters More Than You Think

The way you split documents into chunks fundamentally affects retrieval quality.

Key Recommendations

• Semantic chunking often outperforms fixed-size chunking
• Overlap between chunks (10-20%) helps preserve context
• Metadata attached to chunks (source, date, section) improves filtering
• Hierarchical chunking enables context expansion when needed

Lesson 2: Hybrid Search is Usually Better Than Pure Vector Search

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# Pseudo-code for hybrid search
vector_results = vector_db.search(query_embedding, k=20)
keyword_results = keyword_index.search(query_text, k=20)
combined = reciprocal_rank_fusion(vector_results, keyword_results)
return combined[:10]

Lesson 3: Reranking is Worth the Latency Cost

Initial retrieval optimizes for recall (finding all relevant documents), but reranking optimizes for precision:

• Use a cross-encoder model to re-score top candidates
• The latency cost (50-100ms) is usually acceptable for enterprise applications
• Quality improvements of 10-20% on relevance metrics are typical

Lesson 4: Context Window Management

Lesson 5: Evaluation is Non-Negotiable

You can’t improve what you don’t measure.

Evaluation Framework

1. Build a golden dataset of questions with expected answers
2. Measure both retrieval quality and generation quality
3. Run automated evaluations in CI/CD pipelines
4. Collect user feedback for continuous improvement

Production Considerations

Scaling

Monitoring

Track these metrics in production:

Security

Implementation Checklist

1. Data Pipeline: Document ingestion and chunking strategy
2. Vector Store: Select and configure vector database
3. Retrieval: Implement hybrid search with reranking
4. Generation: Prompt engineering and context assembly
5. Evaluation: Build golden dataset and automated tests
6. Monitoring: Set up observability and alerting
7. Security: Implement access control and audit logging

Conclusion

RAG systems are deceptively simple in concept but require careful engineering to work well in production. The key is to treat RAG as a complete system rather than just connecting a vector database to an LLM.

Start with a strong evaluation framework, iterate on each component, and monitor everything in production. The investment in infrastructure pays off in system reliability and user trust.

Working on a RAG implementation? Reach out to discuss your specific challenges.