Grounding Techniques for LLMs

Are you looking to learn more about large language models (LLMs)? Join instructor Denys Linkov as he explores hallucinations, their causes, the implications they have on the reliability and usability of LLMs, and how to mitigate structural and contextual inaccuracies to ensure high-quality, time-sensitive output. Develop practical techniques for addressing hallucinations, including few-shot learning, model fine-tuning, and templates for guiding LLM outputs. You’ll also delve into more advanced topics like the chain of thought reasoning, retrieval-augmented generation, and model routing to enhance LLM performance. Test out your new skills along the way with real-world challenges that provide hands-on experience to solidify your learning. Whether you’re an AI researcher, a data scientist, or a tech enthusiast intrigued by the evolving capabilities of LLMs, this course offers valuable insights on navigating the complexities of AI with ease.

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time-all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started.