Abhinav Kimothi

Abhinav Kimothi is a seasoned AI practitioner with over 15 years of experience developing cutting-edge AI and machine learning solutions. Throughout his career, Abhinav has led AI projects across analytics, predictive ML, NLP, and generative AI—some were successful, while others provided valuable lessons. Driven by curiosity and a passion for innovation, he continues to push the boundaries of AI to create effective solutions. You can learn more about Abhinav at https://www.abhinavkimothi.com/.

books & videos by Abhinav Kimothi

Master and Build Large Language Models

  • Course duration: 17h 15m

The best way to understand LLMs is to build one yourself. This course gives you that power.

In this engaging liveVideo, veteran AI researcher Sebastian Raschka leads you step by step through the inner workings of a large language model. You'll see and hear Sebastian talk you through each step of the LLM project you build in his bestselling book Build a Large Language Model (From Scratch).

In this liveVideo, you'll explore how to:

  • Plan and code all the parts of an LLM
  • Prepare a dataset suitable for LLM training
  • Fine-tune LLMs for text classification and with your own data
  • Use human feedback to ensure your LLM follows instructions
  • Load pretrained weights into an LLM

This liveVideo is the perfect orientation to LLMs for software engineers ready to lead AI initiatives or data scientists and ML researchers who want build or adapt their own LLMs.

This unique course also includes six essential prerequisite videos created by AI expert Abhinav Kimothi, author of A Simple Guide to Retrieval Augmented Generation. Abhinav delivers an insightful review of everything from the Python features you need to work on LLMs to advanced PyTorch operations, ensuring you can succeed regardless of your starting point.

A Simple Guide to Retrieval Augmented Generation

  • June 2025
  • ISBN 9781633435858
  • 256 pages
  • printed in black & white

A Simple Guide to Retrieval Augmented Generation is a plain-English guide to RAG. The book is easy to follow and packed with realistic Python code examples. It takes you concept-by-concept from your first steps with RAG to advanced approaches, exploring how tools like LangChain and Python libraries make RAG easy. And to make sure you really understand how RAG works, you’ll build a complete system yourself—even if you’re new to AI!