RAG from Scratch — A Data Scientist Builds a Document Q&A System
📰 Medium · RAG
Learn to build a document Q&A system from scratch using RAG and understand the importance of retrieval quality over model quality
Action Steps
- Build a document Q&A system from scratch using RAG
- Understand the concept of embeddings and their role in retrieval quality
- Configure a retrieval system to prioritize quality over model complexity
- Test the system using a dataset to evaluate similarity scores
- Apply techniques to avoid negative similarity scores and improve overall performance
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this article to improve their document Q&A systems and understand the nuances of RAG
Key Insight
💡 Retrieval quality is more important than model quality in building an effective document Q&A system
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🤖 Build a document Q&A system from scratch with RAG! 📊 Prioritize retrieval quality over model quality for better results
Key Takeaways
Learn to build a document Q&A system from scratch using RAG and understand the importance of retrieval quality over model quality
Full Article
What embeddings actually are, why retrieval quality beats model quality, and the mistake that gives you negative similarity scores. Continue reading on Medium »
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