I Spent 6 Months Fixing RAG. Here's What I Found (And Built)
A developer spent six months debugging a Retrieval-Augmented Generation (RAG) system for document Q&A, identifying two key failure modes: semantic drift in query reformulation and context poisoning by irrelevant but similar chunks. To address these issues, they developed a new framework called VORTEXRAG, featuring a seven-layer architecture. Key innovations include Tri-Vector Encoding for richer embeddings, Vortex Retrieval Cone for improved document ranking, and a Semantic Drift Corrector to maintain query intent across multiple hops. AI
IMPACT This new framework offers a potential solution to common RAG system failures, which could improve the reliability of document Q&A and other LLM applications.