Overview
- Developers report that RAG prototypes often collapse in real use because retrieval pipelines misfire even when the underlying LLM is sound.
- Common symptoms include irrelevant context, hallucinated answers, slower responses as corpora grow, and rising operational expense.
- Vector-only search frequently surfaces text that looks related but lacks the precise steps users need, triggering guesswork from the model.
- Naive fixed-size chunking splits structured documents, and many queries require assembling evidence across multiple sources to answer reliably.
- Recommended production pattern layers query understanding, hybrid vector‑keyword retrieval, reranking, and context assembly, with AWS tools like OpenSearch, Bedrock, Lambda, and S3 used to operationalize the workflow.