Let's build LLM-powered AI Agents. In today's fast-paced world of AI, there is an increasing demand for custom AI solutions. This repository is dedicated to helping you develop an intuition about Large Language Models (LLMs). From integrating Retrieval Augmented Generation (RAG) to incorporating specific tools and choosing functionalities, your decisions should closely align with the unique needs of your use cases. To achieve this, businesses must cultivate a profound understanding of LLMs. This repository is designed to provide comprehensive insights into the capabilities, limitations, and workings of LLMs. By the end, you should have a relatively deep knowledge of AI, LLMs, and RAG more generally.
Disclaimer: This repository reflects my personal approach to understanding and leveraging LLMs. It is by no means the only way to utilize LLMs effectively. My aim is to share insights and methods that have worked for me, but there are many other valid and effective strategies. I encourage you to explore various perspectives and find what best suits your needs and use cases. Passion leads to results. Happy hacking! :)
- Chapter 01: Prompting
- Chapter 02: Tools
- Chapter 03: Structured Outputs
- Chapter 04: AI Agents
- Chapter 05: AI Agent Workflows
- Chapter 06: Multi-Agents
- Chapter 07: Building Trust and Transparency in LLMs
- Chapter 08: LLMs integrating with Forecasting Models
- Chapter 09: Working with Unstructured Data
- Chapter 10: Retrieval Augmented Generation (RAG)
- Chapter 11: Evaluating LLM Outputs
- Chapter 12: Generative UI
This repository is released under the MIT license. In short, this means you are free to use this software in any personal, open-source or commercial projects. Attribution is optional but appreciated.