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.
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knowledge with real-time data from documents, websites, or databases, RAG overcomes limitations associated with outdated LLM content.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.
Four Approaches to Improve LLM Results
To maximize the effectiveness of LLMs, a fourfold path for enhancing results has been proposed, encompassing domain-specific models, improved prompting, RAG implementation, and parallel prompting. Domain-specific fine-tuning narrows the focus of LLMs to better suit specific fields like legal research, which can lead to more reliable outputs. Improved prompting involves the strategic crafting of questions to elicit clearer, more relevant responses from the models. The RAG method further optimizes performance by linking generated responses with specific data sources, while parallel prompting allows users to run identical prompts across multiple models for comparative insights.
Innovative AI Tools: Practical Applications
Several innovative AI tools exemplify the RAG approach, notably perplexity.ai, Google Notebook LM, and Westlaw's Practical Law with AI. Perplexity.ai functions as a thoughtful search engine that provides users with well-reasoned answers while citing sources, making it particularly effective for research-related queries. Google Notebook LM enables users to organize and analyze large volumes of documents by generating study guides, FAQs, and briefings from uploaded materials, creating a seamless and efficient information retrieval process. Westlaw’s Practical Law integrates AI with curated legal data, enhancing the research process and providing law students and practitioners with invaluable summaries, templates, and practice-specific insights.
Shifting Perceptions of AI in the Legal Profession
Addressing worries among senior lawyers regarding AI's impact on traditional legal skills is essential for fostering acceptance of these new technologies. Emphasizing AI as a tool that amplifies rather than replaces expertise can help senior professionals recognize its value in enhancing efficiency and improving client interactions. Specific examples, such as how AI can assist with drafting arguments or conducting quick legal research, provide concrete demonstrations of the technology's utility. Framing the conversation around AI as a natural evolution of legal technology, similar to the shift from physical law books to online databases, can further facilitate understanding and encourage adoption among hesitant practitioners.
As you may have discovered on your own, genAI tools are ready and enthusiastic with their outputs, but may be woefully ill-informed, in spite of the snappy replies they spew out with unfettered confidence. So, what is being done to remedy this issue? Dennis and Tom explain how Retrieval-Augmented Generation (RAG) combines AI’s LLMs with more current external information, addressing problems arising from outdated LLM data. The guys talk through some of their favorite tools that employ RAG effectively and offer insights into their uses for attorneys.
Later, could AI-adoption be diminishing a lawyer’s hard-earned expertise? Dennis and Tom dive into this common fear shared by many traditionally-minded attorneys, focusing on ways to leverage AI not to replace, but enhance their legal practice.
As always, stay tuned for the parting shots, that one tip, website, or observation you can use the second the podcast ends.
Have a technology question for Dennis and Tom? Call their Tech Question Hotline at 720-441-6820 for the answers to your most burning tech questions.
Show Notes - Kennedy-Mighell Report #372
A Segment: AI and RAG: Hate the Name, Love the Application