How AI Is Built

#033 RAG's Biggest Problems & How to Fix It (ft. Synthetic Data)

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Nov 28, 2024
Saahil Ognawala, Head of Product at Jina AI and expert in RAG systems, dives deep into the complexities of retrieval augmented generation. He reveals why RAG systems often falter in production and how strategic testing and synthetic data can enhance performance. The conversation covers the vital role of user intent, evaluation metrics, and the balancing act between real and synthetic data. Saahil also emphasizes the importance of continuous user feedback and the need for robust evaluation frameworks to fine-tune AI models effectively.
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INSIGHT

RAG: Not a Magic Bullet

  • RAG systems aren't production-ready out-of-the-box solutions.
  • They require systems, metrics, and processes for optimization.
ADVICE

Define Bad Results

  • Define bad results explicitly in your retrieval benchmarks, including hallucinations.
  • This helps identify seemingly correct but factually wrong answers.
ANECDOTE

Insurance Chatbot Example

  • An insurance chatbot example highlights how LLMs can generate plausible yet incorrect answers.
  • Evaluation benchmarks must address this by identifying such cases.
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