This Podcast offers an introduction to Large Language Models (LLMs), explaining them as advanced autocomplete systems that predict the next word based on vast amounts of text. It distinguishes between the expensive training phase of LLMs, where models learn from the entire internet, and the cheaper inference stage, where they process user requests. The speaker emphasizes the importance of prompts—the instructions given to an LLM—highlighting how clear, well-structured prompts with examples and constraints lead to better outputs, and introduces the concept of prompt engineering as a developing skill. The discussion then moves to vector databases and Retrieval Augmented Generation (RAG), a method for improving LLM accuracy and reducing hallucinations by providing models with relevant, up-to-date information from external sources, often described as "AI search." Finally, the video introduces Model Context Protocol (MCP) as a standardized way for LLMs to safely interact with various tools and data, and agents and agentic frameworks as systems that enable LLMs to perform complex, multi-step actions and workflows, even within platforms like Drupal.
NoteBook to interact with: https://notebooklm.google.com/notebook/1cffd272-9c9d-43fc-ae72-49579b85b9fd?artifactId=40d19c5b-33f5-4857-8c10-5ef729954203
Credits: Source Video: https://youtu.be/zv2ht2jHXvA
Video Sponsors: https://www.drupalforge.org/
Infrastructure, tooling, and AI provider by https://devpanel.com/
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This episode of DrupalBrief is sponsored by DrupalForge.org
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