DataFramed #226 Creating Custom LLMs with Vincent Granville, Founder, CEO & Chief Al Scientist at GenAltechLab.com
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Jul 15, 2024 
Vincent Granville, Founder and CEO of GenAI Tech Lab and a prominent figure in the AI field, discusses the intriguing world of custom large language models (LLMs). He highlights the limitations of standard LLMs and the unique advantages of developing custom solutions tailored to specific corporate needs. The conversation dives into the importance of knowledge graphs, ethical considerations, and the evolving dynamics of AI in information retrieval. Vincent also shares his insights on navigating the complexities of LLM development, including legal and technical challenges.
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Initial Motivation for Custom LLMs
- Vincent Granville found standard LLMs like Google Search and OpenAI's GPT lacking in technical depth.
- They often provided generalized answers, unsuitable for expert users seeking specialized information.
Benefits of Custom LLMs
- Custom LLMs can be designed to provide concise, precise answers for specific domains or user types.
- Granville's XLLM prioritizes structured outputs with relevance scores, allowing customized results for different users.
Corporate Use Case: InBev
- InBev, a Fortune 100 company, sought a custom LLM for local implementation, prioritizing security and latency.
- Concerns about data leakage, liability from hallucinations, and ease of implementation drove their interest.
