I Spent 6 Months Fixing RAG. Here's What I Found (And Built)
The article discusses the author's six-month journey to improve a document Q&A system using a Retrieval-Augmented Generation (RAG) framework. The author identified two main failure modes: semantic drift and context poisoning, which led to incorrect answers despite having the right documents. To address these issues, the author developed a new framework called VORTEXRAG, which includes multiple layers targeting specific problems in the retrieval process.
- ▪The author encountered significant issues with a document Q&A system when it provided incorrect answers to complex queries.
- ▪Two primary failure modes were identified: semantic drift, which led to retrieving irrelevant documents, and context poisoning, where contradictory information derailed the model's accuracy.
- ▪The new framework, VORTEXRAG, features seven layers designed to tackle these specific failure modes and improve the overall performance of the retrieval system.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3935700) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } vigneshwar Posted on May 19 • Originally published at github.com I Spent 6 Months Fixing RAG. Here's What I Found (And Built) #llm #ai #python #machinelearning This is the story of a debugging session that turned into a research paper. The Bug That Started Everything I was building a document Q&A system — nothing exotic. Standard RAG setup. FAISS index, SBERT embeddings, GPT as the reader. Classic. It worked fine on simple questions. "What is the refund policy?" → correct answer.
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