Retrieval-Augmented Generation (RAG) has emerged as the most practical approach to building AI systems that need access to current, domain-specific knowledge. But moving from a prototype to production requires careful architectural decisions.
Why RAG Matters
Large language models are powerful, but they have a fundamental limitation: their knowledge is frozen at training time. RAG solves this by combining the reasoning capabilities of LLMs with real-time retrieval from your own data sources.
The result is an AI system that can answer questions about your latest documentation, recent customer interactions, or any other proprietary data — without the cost and complexity of fine-tuning.
The AWS Stack
Our production RAG deployments typically use Amazon Bedrock for the LLM layer, Amazon OpenSearch for vector search, and AWS Lambda for orchestration. This combination provides the best balance of performance, cost, and operational simplicity.
Bedrock gives you access to Claude, Titan, and other leading models without managing infrastructure. OpenSearch handles vector similarity search at scale, and its serverless option eliminates capacity planning headaches.
Key Implementation Patterns
Chunk size matters more than you think. We typically start with 512 tokens with 50-token overlap, then tune based on your specific content. Technical documentation often benefits from larger chunks; conversational data works better with smaller ones.
Hybrid search — combining keyword and vector retrieval — consistently outperforms pure vector search. OpenSearch makes this straightforward with its neural search plugin.
Monitoring and Iteration
Deploy with comprehensive logging from day one. Track retrieval relevance, generation quality, and user feedback. These metrics guide your iteration cycle and help identify when reindexing or prompt adjustments are needed.
Production RAG is never "done" — it requires ongoing attention to maintain quality as your data evolves.