Weekly Episode Summaries and Programming Notes – Week of October 29, 2023
Oct 29, 2023
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The podcast discusses practical applications of LLMs and the concept of DataMesh. Madoff Shrinoff talks about the early stage of generative AI and challenges of using large language models. The chapter also explores JNI models, layered LLMs, and their cost-effective implementation.
Large Language Models (LLMs) can be cost-effective when run in a serverless model, where organizations only pay per query.
Starting with existing open source models and gradually adding tighter, more specific inputs can improve the performance of LLMs on specific tasks.
Deep dives
Practical Applications of Gen AI
In episode 264, an interview with Madoff Srinod explores the practical applications and approaches of Large Language Models (LLMs) and how they can be useful for implementing data. The discussion highlights the cost-effectiveness of running LLMs in a serverless model, where organizations only pay per query. Furthermore, the episode emphasizes the importance of having highly specific LLMs that serve a particular purpose rather than generalized models. It is noted that many organizations are not training their own LLMs but leveraging the availability of excellent open source models in the industry.
Starting with Domain-Specific Problems
The episode suggests that organizations should begin their Gen AI work by focusing on domain-specific problems. This approach involves feeding LLMs with tighter and more specific inputs related to business areas. By keeping the focus narrow at the start and gradually adding additional topics, organizations can avoid overwhelming the models and improve the performance of LLMs on specific tasks. It is also highlighted that LLMs are not magic and require human oversight to ensure quality and prevent errors or hallucinations in their generated answers.
Leveraging Open Source Models and Layered LLMs
The episode also advises organizations to leverage existing open source models rather than attempting to train LLMs from scratch. By starting with a base model and tuning it based on their own answers, organizations can save time and effort. Additionally, the episode discusses the concept of layered LLMs, where one model is dedicated to answering data-related questions and another model focuses on ensuring correctness, governance, security, and privacy. It is emphasized that LLMs are cost-effective to run and most organizations do not need to train their own models when industry advances in open source models are readily available.