# An Open Course on LLMs, Led by Practitioners

DevFeed: [An Open Course on LLMs, Led by Practitioners](<https://devfeed.tech/articles/an-open-course-on-llms-led-by-practitioners-18785.md>)

Original publisher: [Read original article](<https://hamel.dev/blog/posts/course/>)

Author: Hamel Husain

Published: 2024-07-29T07:00:00Z

Content type: release

Language: en

Sources: [Hamel Husain](<https://devfeed.tech/sources/hamel-husain.md>)

Topics: [LLMs](<https://devfeed.tech/topics/llms.md>), [Tutorial](<https://devfeed.tech/topics/tutorial.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [Data Science](<https://devfeed.tech/topics/data-science.md>), [MLOps](<https://devfeed.tech/topics/mlops.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>)

Tags: [course](<https://devfeed.tech/tags/course.md>), [courses](<https://devfeed.tech/tags/courses.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [free](<https://devfeed.tech/tags/free.md>), [guide](<https://devfeed.tech/tags/guide.md>), [llms](<https://devfeed.tech/tags/llms.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [open](<https://devfeed.tech/tags/open.md>), [rag](<https://devfeed.tech/tags/rag.md>), [recommendation-systems](<https://devfeed.tech/tags/recommendation-systems.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

## AI overview

The article announces Mastering LLMs, a free, open course of workshops and talks led by more than 25 industry practitioners. It covers applied topics including evaluations, retrieval-augmented generation, fine-tuning, application development, and prompt engineering, and is intended for technical professionals with basic LLM experience.

## Source excerpt

Today, we are releasing Mastering LLMs, a set of workshops and talks from practitioners on topics like evals, retrieval-augmented-generation (RAG), fine-tuning and more. This course is unique because it is: Taught by 25+ industry veterans who are experts in information retrieval, machine learning, recommendation systems, MLOps and data science. We discuss how this prior art can be applied to LLMs to give you a meaningful advantage. Focused on applied topics that are relevant to people building AI products. Free and open to everyone . We have organized and annotated the talks from our popular paid course.1 This is a survey course for technical ICs (including engineers and data scientists) who have some experience with LLMs and need guidance on how to improve AI products. Speakers include Jeremy Howard, Sophia Yang, Simon Willison, JJ Allaire, Wing Lian, Mark Saroufim, Jane Xu, Jason Liu, Emmanuel Ameisen, Hailey Schoelkopf, Johno Whitaker, Zach Mueller, John Berryman, Ben Clavié, Abhishek Thakur, Kyle Corbitt, Ankur Goyal, Freddy Boulton, Jo Bergum, Eugene Yan, Shreya Shankar, Charles Frye, Hamel Husain, Dan Becker and more Getting The Most Value From The Course Prerequisites The course assumes basic familiarity with LLMs. If you do not have any experience, we recommend watching A Hacker's Guide to LLMs. We also recommend the tutorial Instruction Tuning llama2 if you are interested in fine-tuning 2. Navigating The Material The course has over 40 hours of content. To help you navigate this, we provide: Organization by subject area: evals, RAG, fine-tuning, building applications and prompt engineering. Chapter summaries: quickly peruse topics in each talk and skip ahead Notes, slides, and resources: these are resources used in the talk, as well as resources to learn more. Many times we have detailed notes as well! To get started, navigate to this page and explore topics that interest you. Feel free to skip sections that aren't relevant to you. We've organized the talks