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Retrieval augmented fine-tuning and data integrations

  • Dan Wilson 

Presentation and discussion with Suman Aluru and Caleb Stevens

Retrieval augmented fine-tuning and data integrations

In the latest episode of the “AI Think Tank Podcast,” I had the pleasure of hosting a deep dive into the world of AI advancements, specifically focusing on “RAFT” (Retrieval Augmented Fine Tuning). Joining me were the esteemed guests Suman Aluru and Caleb Stevens, who both have much to do with AI infrastructure and application. Our conversation revolved around how RAFT bridges the critical gaps between fine-tuning and retrieval-augmented generation (RAG), and the significant impact this has on AI-driven applications.

We opened the episode by discussing the importance of RAFT in the current AI landscape, where Suman eloquently described its role in enhancing the accuracy of AI responses and reducing the common errors known as “hallucinations.” Caleb complemented this by highlighting the practical deployment of RAFT in IT infrastructures, particularly its effectiveness in managing semantic data where traditional databases might struggle.

A pivotal moment of our discussion was Suman’s live demonstration, which involved querying a model fine-tuned with data from the AI Think Tank podcast’s website. This not only showcased RAFT’s real-world applicability but also demonstrated its power in maintaining the relevancy of AI systems with updated data, eliminating the need for extensive retraining.

Retrieval augmented fine-tuning and data integrations

Figure-1 Cited from https://arxiv.org/abs/2403.10131 Download full pdf here.

We also delved into the challenges associated with updating AI models post-training. Here, RAFT was discussed as a dynamic solution capable of integrating fresh data seamlessly, thus enabling AI systems to process complex queries with enhanced contextual understanding. The discussion on vector databases and embedding techniques provided a clear insight into the technological strategies that make RAFT a standout choice.

Retrieval augmented fine-tuning and data integrations

Figure-2 Cited from https://arxiv.org/abs/2403.10131 Download full pdf here.

The episode wrapped up with an engaging Q&A session where our listeners had the opportunity to probe deeper into the applications of RAFT, its advantages over traditional AI training techniques, and its potential transformative impact across various sectors.

Overall, this episode offered a thorough exploration of how advanced techniques like RAFT can significantly bolster the functionality and reliability of AI systems, ensuring they perform domain-specific tasks more effectively and with greater accuracy. The feedback from our community was immensely positive, highlighting the importance and interest in such cutting-edge technologies in the AI space.

As usual, I gained much insight from Suman’s presentations and Caleb’s keen understanding at the code level. We expect to continue this exploration of RAG and RAFT as things develop.

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