I rebuilt my Financial Mentor retrieval from scratch. Here's everything the RAG stack taught me
The article discusses the author's experience rebuilding the Financial Mentor retrieval system using the RAG stack. It highlights the challenges faced with data indexing and retrieval accuracy, particularly in the context of financial data. The author emphasizes the importance of addressing vocabulary mismatches and ensuring real-time data accuracy for better user experience.
- ▪The original system provided irrelevant data, leading to confusion and inaccuracies in user queries.
- ▪The author implemented a three-stage process to improve data retrieval, focusing on relevant indexing and semantic chunking.
- ▪Real-time financial data must be fetched at query time to avoid accuracy issues caused by stale indexes.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3929476) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Saulo Linares Posted on May 21 I rebuilt my Financial Mentor retrieval from scratch. Here's everything the RAG stack taught me #ai #claude #machinelearning #python From stuffing JSON into Claude to GraphRAG, hybrid search, CRAG, and adversarial evaluation — the complete honest account The problem with FinMentor started before I had the vocabulary to describe it... Users were asking reasonable questions about their portfolios. The system was answering them. Some answers were right.
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