Building RAG Systems That Actually Work

Retrieval-Augmented Generation (RAG) has become the go-to architecture for building LLM applications that need access to private or recent data. But getting RAG to work well in production is harder than it looks.

The Promise of RAG

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RAG combines the power of large language models with the accuracy of retrieval systems. In theory, it's the best of both worlds.

Common Pitfalls

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1. Chunking Gone Wrong

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2. Retrieval Quality Issues

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3. Context Window Overflow

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What Actually Works

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• Hybrid Search: Combine dense and sparse retrieval
• Smart Chunking: Respect document structure
• Re-ranking: Use cross-encoders for precision
• Evaluation: Measure retrieval AND generation quality

Production Tips

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Building great RAG systems is an iterative process. Start simple, measure everything, and improve incrementally.