Bring Your Own Data to LLMs (W/ Jerry Liu of LlamaIndex)
Aug 25, 2023
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Jerry Liu, CEO and co-founder of LlamaIndex, discusses how companies are bringing their data to tailor large language models (LLMs) for their needs. Topics include working on LLMs versus autonomous systems, skill set and data preparation for LOMs, using databases for storing embeddings, capabilities of LMs in analyzing user questions, and exploring agents and specialized microservices in analytics engineering.
Building a framework that allows organizations to tailor large language models (LLMs) to their specific needs is crucial.
LLMs can provide immediate value by processing text, making them more accessible for software developers compared to autonomous systems like self-driving cars.
Deep dives
Building a Framework for LLM Applications
Jerry Liu, CEO and co-founder of LAMA Index, discusses the importance of building a framework that allows organizations to tailor large language models (LLMs) to their specific needs. LAMA Index is an open source framework that helps people prepare their data for use with LLMs through retrieval augmented generation. Jerry emphasizes the value of adding additional context and knowledge to LLMs to make them more useful for businesses.
The Intersection of AI and Autonomous Systems
Jerry Liu highlights the similarities and differences between AI and autonomous systems. While there are overlapping concepts, such as working on interesting problems and using deep learning models, autonomous systems, like self-driving cars, present unique challenges due to the complexity of robotics and real-world interactions. In contrast, LLMs can provide immediate value by processing text, making them more accessible for software developers.
Data Preparation for LLMs with Retrieval Augmented Generation
Jerry Liu explains the two-step process for preparing data for LLMs using retrieval augmented generation. The first step involves retrieving relevant content from a vector database based on embedding similarity. The second step is synthesis, where the retrieved context is used as input for the LLM to generate responses or answers to specific prompts. Jerry also mentions the challenge of fitting data into the context window, which is the limit for input and output text in LLMs.
The Future of LLMs and Agent Architectures
Jerry Liu speculates on the future of LLMs and agent architectures. He predicts a world where multiple specialized agents communicate and work together to solve tasks, rather than relying on a single general-purpose LLM. He envisions agent-based systems that leverage the reasoning capabilities of LLMs for specific functions and utilize communication protocols between agents to achieve greater capabilities. Jerry believes that this approach, along with advancements in model technology and API design, will lead to more reliable and practical LLM applications.
Jerry Liu is the CEO and co-founder of LlamaIndex. LlamaIndex is an open-source framework that helps people prep their data for use with large language models in a process called retrieval augmented generation. LLMs are great decision engines, but in order for them to be useful for organizations, they need additional knowledge and context, and Jerry discusses how companies are bringing their data to tailor LLMs for their needs.
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The Analytics Engineering Podcast is sponsored by dbt Labs.
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