Wrote up the failure modes that kept breaking my RAG system: chunking, stale index, hybrid search, the works

A user shared their experience troubleshooting a Retrieval-Augmented Generation (RAG) system that frequently produced incorrect answers. The primary issues arose from chunking methods; fixed-size chunks either lacked context or buried relevant information under excessive text. By switching to a sliding window approach and utilizing semantic chunking for vital documents, they achieved better results. However, this increased costs, highlighting the need for balance between efficiency and accuracy.

Another critical failure point was a stale index, where outdated documents led to misleading responses. The user resolved this by establishing automatic re-indexing and combining semantic and keyword searches to enhance retrieval accuracy. They also learned the importance of providing explicit instructions in system prompts to prevent hallucinations from the LLM. By passing contextual information along with chunks, they significantly improved the quality of responses, especially for longer documents. The user invites feedback from others who may have faced similar challenges, particularly regarding the stale index issue.