Welcome to the comprehensive course on advancing your skills in building sophisticated Large Language Model (LLM) applications!

We have tried to build the most advanced LLM course currently being offered in the world. No pun intended.

If you have already acquired knowledge about RAG, cosine similarity, vector databases, and Langchain, it's time to delve into the practical aspects of packaging and deploying these models in production environments.

This course builds upon the fundamental building blocks of LLMs and covers the following key topics:

1. Fine-tuning: Learn advanced techniques for fine-tuning LLMs (ChatGPT and Open-source LLMs) to enhance their performance and adapt them to specific tasks or domains.

2. Model merging: Explore methods to merge multiple models, optimizing their collective capabilities for more robust and versatile language processing.

3. Inference speed exploration: Understand strategies to optimize and accelerate inference speeds, ensuring efficient real-time processing of language model outputs.

4. Quantization methods: Dive into techniques for model quantization, reducing model size while maintaining performance, crucial for deployment in resource-constrained environments.

5. Model hosting and deployments: Gain insights into best practices for hosting and deploying LLMs in production settings, ensuring seamless integration into diverse applications.

6. Semantic Caching: Learn how to build it all from scratch and implement it with GCP and REDIS

7. Guardrail and DSPy: Implement State of the Art Guardrail and learn how you can build applications with minimal prompting

Throughout the course, we will analyze state-of-the-art AI products, reverse-engineering some through Python.

Additionally, my collaboration with experienced Software Engineers on our team will provide valuable insights into integrating LLMs with Node.js for web application development.

As a bonus, you'll have access to experimental products being developed at Traversaal.ai, my startup, allowing you to stay at the forefront of cutting-edge advancements in the field.

Prerequisites for this course include proficiency in Python and a solid understanding of RAGs, as well as Encoder and Decoder models.

If you feel the need for a more foundational course, consider checking out my other offering on LLMs: https://maven.com/boring-bot/ml-system-design

Tools utilized in this course include VS Code, UNIX terminal, Jupyter Notebooks, and Conda package management, ensuring a hands-on and practical learning experience.