Episode 8: Prompt Engineering vs RAG vs Finetuning

The conversation covers the importance of prompt engineering, the role of prompting in AI model performance, the use of keyword search for refining AI outputs, and the introduction to Retrieval Augmented Generation (RAG) for further refinement. The conversation delves into the technical aspects of data storage, canonicalization, and the use of MariaDB for vector store and operational data. It emphasizes the importance of efficiency and cost considerations in refining RAG systems and the need for human involvement in AI models. The discussion also explores the purpose and benefits of fine-tuning AI models, an iterative approach to AI model development, scaling, system integration, and the future of AI technologies.TakeawaysPrompting is crucial for AI model performanceKeyword search and RAG are important for refining AI outputs Canonicalization and normalization reduce the amount of embedded logs by 70%Fine-tuning AI models requires a clear understanding of the desired output and iterative testingChapters00:00 Introduction to Prompt Engineering07:15 Using Keyword Search13:00 Introduction to RAG24:59 Data Storage and Canonicalization33:10 Understanding Fine-Tuning of AI Models40:18 Iterative Approach to AI Model Development49:54 Edge Technologies and Future of AI