The Kennedy-Mighell Report cover image

The Kennedy-Mighell Report

AI and RAG: Hate the Name, Love the Application

Nov 1, 2024
Explore the exciting world of Retrieval-Augmented Generation (RAG) and how it enhances AI outputs with up-to-date information. Discover innovative tools like Perplexity.ai and Google Notebook LM that boost accuracy in legal research. The discussion also tackles the fear that AI could diminish lawyers' expertise, encouraging a mindset that sees AI as an ally rather than a replacement. Finally, learn about the evolving role of users in AI tool ecosystems and practical applications like Google's camera search that simplify everyday tasks.
47:41

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Quick takeaways

  • Retrieval-Augmented Generation (RAG) enhances large language models by linking them with external data for more accurate and relevant outputs.
  • Emphasizing AI as a tool that amplifies rather than replaces legal expertise helps mitigate concerns among traditionally-minded attorneys about technology adoption.

Deep dives

The Rise of Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is emerging as a significant approach in AI application, particularly for improving the output of large language models (LLMs). This method enhances the responses of LLMs by connecting them with external, more current information sources, enabling users to receive accurate and context-aware results. By combining the model's pre-trained knowledge with real-time data from documents, websites, or databases, RAG overcomes limitations associated with outdated LLM content. Practical examples illustrate this, such as asking an LLM for the best diner in a city and then comparing the answer with recent reviews from a site like Yelp for validation.

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