Engineering RAG Systems That Actually Work: Conversational Retrieval, Page Awareness & Debugging (Part 5)
The article discusses advancements in building Retrieval-Augmented Generation (RAG) systems, focusing on conversational retrieval and debugging. It highlights the transition from a basic query-response model to a more context-aware and interactive system. Key improvements include handling follow-up questions and ensuring relevant retrieval from documents.
- ▪The article is part of a series on building RAG systems from scratch.
- ▪It addresses issues like follow-up questions not working well and retrieval returning irrelevant chunks.
- ▪The goal is to enhance the system's ability to understand context and validate answers.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3855663) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Sharath Kurup Posted on May 18 Engineering RAG Systems That Actually Work: Conversational Retrieval, Page Awareness & Debugging (Part 5) #python #ai #rag #tutorial Building RAG Systems from Scratch (ChatPDF Series) (5 Part Series) 1 Understanding RAG by Building a ChatPDF App with NumPy (Part 1) 2 Understanding RAG by Building a ChatPDF App: From NumPy to FAISS (Part 2) 3 Understanding RAG by Building a ChatPDF App: Smarter Chunking & Context Optimization (Part 3) 4 Part 4:…
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