rag Blog Posts
Retrieval-Augmented Generation connects LLMs to your actual data — but the retrieval side is where most RAG implementations fall apart. Chunking strategy, embedding model selection, index design, hybrid search, and re-ranking all have a direct impact on answer quality. Building a prototype is straightforward; building a system that stays accurate as your data changes, handles edge cases gracefully, and doesn't hallucinate requires real engineering discipline. Our articles cover production RAG patterns, evaluation frameworks, vector store selection, and the tradeoffs that matter at scale. If you're building a RAG system on top of OpenSearch or Elasticsearch, our AWS GenAI RAG Fast Track helps teams go from prototype to production.
