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How AI Is Built

#33 Saahil Ognawala on RAG's Biggest Problems & How to Fix It (ft. Synthetic Data) | Search

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.
51:26

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Podcast summary created with Snipd AI

Quick takeaways

  • Building an effective RAG system involves structured processes and measurable metrics to meet user intent and needs accurately.
  • Utilizing a balanced mix of synthetic and real-world data in training datasets enhances model performance and adaptability in diverse scenarios.

Deep dives

Importance of Systems in Retrieval Augmented Generation

Building an effective retrieval augmented generation (RAG) system requires more than simply applying embeddings to existing data. It necessitates a well-structured system that includes defined processes and measurable metrics to achieve quantitative success. Understanding user intent is at the core of optimizing RAG, as it allows system designers to tailor outputs to better meet user needs. Simply relying on embeddings without a deeper framework can lead to inadequate or irrelevant results that fail to serve the users effectively.

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