This repository provides a comprehensive, free guide for individuals with a basic programming background to become proficient in Large Language Models (LLMs). It curates a structured learning path covering foundational concepts, practical applications like Prompting and Retrieval Augmented Generation (RAG), and career advice, aiming to keep users updated with the latest advancements.

How It Works

The guide follows a modular approach, allowing users to select resources based on their learning preferences (videos, books, online courses). It emphasizes a "learn by doing" philosophy, recommending practical projects and leveraging tools like ChatGPT and GitHub Copilot as learning aids. Key topics include understanding LLM terminology, transformer architectures, prompt engineering, RAG, and fine-tuning.

Quick Start & Requirements

• Prerequisites: Basic Python and AI knowledge recommended. A separate guide is linked for those with zero background.
• Resources: Primarily free online resources (YouTube videos, articles, free courses), with optional paid courses and books.
• Setup: No specific software installation required for the guide itself; learning resources may have their own requirements.
• Links: YouTube Video Introduction, Official Guide

Highlighted Details

• Curated list of free YouTube videos, podcasts, and online courses from reputable sources like Andrej Karpathy, Luis Serrano, Cohere, and freeCodeCamp.
• Detailed sections on Prompt Engineering, Retrieval Augmented Generation (RAG), and Fine-Tuning with practical examples and resources.
• Includes advice on finding ML jobs, AI ethics, and staying updated with AI news through newsletters and communities.
• Encourages practical application through project building and use of AI coding assistants.

Maintenance & Community

• Maintained by louisfb01, who is active on YouTube and LinkedIn.
• Open to community contributions via GitHub issues for new resources.
• Links to relevant Discord servers and Reddit communities for AI enthusiasts.

Licensing & Compatibility

• The repository itself is likely under a permissive license (e.g., MIT, Apache 2.0) as it aggregates external resources. Specific resource licenses vary.
• Compatibility for commercial use depends on the licenses of the linked external resources.

Limitations & Caveats

The guide is a curated list of external resources, not a self-contained learning platform. While it aims to be comprehensive, the rapidly evolving nature of LLMs means some information may become dated. The author notes that some linked course/book resources are affiliated.