The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Why Your RAG System Is Broken, and How to Fix It with Jason Liu - #709
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Nov 11, 2024 Jason Liu, a freelance AI consultant and creator of the Instructor library, dives deep into retrieval-augmented generation (RAG) systems. He shares key signs of RAG system failures and the tactical steps to diagnose issues. Liu emphasizes building robust test datasets and the importance of data-driven experimentation. He discusses fine-tuning strategies, chunking techniques, and collaboration tools, while also showcasing how future AI models could revolutionize the space. Finally, he highlights his passion for teaching through his AI consulting course.
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Focus on Retrieval Quality
- Ban adjectives during stand-up to focus on quantifiable metrics.
- Shift focus from generation output to retrieval quality using precision and recall.
Fast and Cheap Evaluations
- Evaluating RAG systems with LLMs as judges shifts the focus from relevancy to prompting.
- Prioritize fast, cheap evaluations for rapid iteration and testing.
Building Simple Test Datasets
- Generate synthetic questions from text chunks to create a simple test dataset.
- Test if the generated questions retrieve the original chunks, giving engineers permission to trust their intuition.