Free LLM and Gen AI courses to take in 2025

Looking for free LLM and Gen AI courses to dive into? Here are 10 online courses that focus on the practical side of building with LLMs. Our picks prioritize hands-on learning and accessibility: each course is either free or offers open access to its materials. We hope this list helps you find your next learning adventure.

Think we’ve missed a great one? Let us know! Our Discord is the best place to share your suggestions.

Disclaimer: All course information is sourced from their official websites. We simply put it together.

LLM evaluation

LLM evaluations for AI builders: applied course

You can go through all the materials at your own pace.
Author: Evidently AI
Duration: 3 hours
Price: free.
Course certificate: yes (for cohort participants).
The course syllabus is available here.

LLM evaluation for AI builders is a new applied LLM course created by the team that develops Evidently, an open-source evaluation and observability framework for ML and LLM systems. 

This LLM course covers core LLM evaluation workflows, such as building test datasets, comparing prompts and models, and tracing LLM outputs. It is practice-oriented and consists of 10 end-to-end code tutorials, from custom LLM judges to RAG evaluations to adversarial testing. 

The course program is designed for AI/ML engineers and everyone building real-world LLM apps. Basic Python skills are required.  

The course materials are available on the YouTube playlist.

Intro to LLM evaluations for AI product teams

You can go through all the materials at your own pace.
Author: Evidently AI
Duration: 1 hour
Price: free.
Course certificate: yes (for cohort participants).
The course syllabus is available here.

LLM evaluations for AI product teams from Evidently is an opportunity to learn everything you need to know to get started with LLM evaluations in under 1 hour!

This course is a gentle introduction to LLM evaluation for AI leaders. It explores the fundamentals of LLM evaluations and observability, from evaluation methods and benchmarks to LLM guardrails and LLM judges. 

You will learn to build LLM evaluation datasets, use synthetic data to run LLM evals, create custom LLM evaluators, and check your app for AI risks and vulnerabilities. The course also covers practical examples of evaluating complex LLM systems like RAGs, Q&A systems, and AI agents. 

The course is designed for AI product managers and everyone interested in grasping the core concepts of LLM quality and observability. No coding skills are required to take the course. 

The course materials are available on the YouTube playlist.

Introduction to Gen AI and LLMs

Generative AI for beginners from Microsoft

You can go through all the materials at your own pace. 
Author: Microsoft
Duration: 21 lessons
Course certificate: no.
Price: free.
The course syllabus is available here.

Generative AI for Beginners from Microsoft focuses on the fundamentals of building Generative AI applications. It covers key topics such as prompt engineering, responsible AI practices, AI app lifecycle, and LLMOps techniques. It also guides through the development of real-world applications, including chatbots, text and image generators, and search tools.

The course consists of 21 theoretical lessons explaining GenAI concepts and practical lessons with code examples and tutorials. Each lesson includes a short video introduction to the topic, a written summary, code samples, and links to extra resources to continue your learning. 

Generative AI for Beginners materials are available in the course’s GitHub repo.

Introduction to Generative AI from Google

You can go through all the materials at your own pace.
Author: Google Cloud
Duration: 8 hours
Course certificate: no.
Price: free.
The course syllabus is available here.

Introduction to Generative AI is a learning path from Google Cloud. It includes three microlearning courses explaining the basics of Generative AI, LLMs, and responsible AI. 

The learning path begins with an overview of Generative AI, its applications, and how it differs from traditional machine learning. Next, learners dive into LLMs, their use cases, and techniques to enhance LLM performance. The final part focuses on Responsible AI, introducing Google's AI principles and ethical considerations in AI development. 

The path also includes two hands-on courses on prompt design in Vertex AI and operationalizing responsible AI practices with Google Cloud.

The course materials are available on the Google Cloud Skills Boost platform.

LLM explainers from Andrej Karpathy

While it's not technically a course, Andrej Karpathy’s LLM video explainers are fantastic. If you’re new to the field and want to understand key LLM concepts in plain English, we highly recommend checking them out.

Intro to LLMs explains how LLMs work, how they're trained, and their real-world applications. It also covers LLM fine-tuning and customization, as well as Generative AI failure modes like jailbreaks and prompt injection. 

Deep dive into ChatGPT is a deeper dive into how LLMs work. It covers how the models are developed, how to think about their "psychology," and how to make the most of them in AI applications.

How I use LLMs is a practical guide on using LLMs in real-world scenarios, from internet search and data analytics to coding and video generation. 

Gen AI and LLMs for builders

LLM course from Stanford

You can go through all the materials at your own pace.
Author: Stanford University
Course certificate: no.
Price: free.
The course syllabus is available here.

Stanford’s LLM course dives into how LLMs work, scale, and impact the world. It covers their architecture, capabilities, training methodologies, ethical considerations, and system-level challenges. The curriculum also includes LLM scaling laws, security concerns, and environmental impacts. 

The course also includes two projects on LLM building and evaluation, which you can implement to build practical skills. 

Course lectures are available in the course’s GitHub repo.

LLM course by Maxime Labonne

You can go through all the materials at your own pace.
Author: Maxime Labonne
Course certificate: no.
Price: free.
The course syllabus is available here.

The LLM course is a collection of educational resources for those wanting to enter the LLM field. It features two main roadmaps: LLM Scientist and LLM Engineer. 

‍The LLM Scientist roadmap focuses on building LLMs. It covers LLM architecture, fine-tuning, preference alignment, model-based and human evaluation, and quantization. The track also highlights emerging trends like multimodality and interpretability. 

‍The LLM Engineer roadmap focuses on creating LLM-based applications ready for production use. It examines how to run LLMs, build vector storage, augment models, and deploy them. The topics also include advanced RAG techniques, AI agents, and LLM safety. 

There is also an interactive version of this course with an LLM assistant that answers questions and tests your knowledge. To chat with the assistant, you can use HuggingChat or ChatGPT.

The LLM course roadmaps are available in this GitHub repo.

LLM Twin course

You can go through all the materials at your own pace.
Author: Decoding ML
Course certificate: no.
Price: free.
The course syllabus is available here.

The LLM Twin Course is a free, open-source program from the authors of Decoding ML that teaches you how to build a production-ready AI assistant that mimics your writing style and personality. It covers the entire lifecycle of a Retrieval-Augmented Generation (RAG) system, including data collection, fine-tuning, inference, and deployment. The course emphasizes LLMOps best practices, such as experiment tracking, model versioning, prompt monitoring, and evaluation. 

The course consists of 12 lessons on engineering practices and end-to-end system implementation. It is designed for ML/AI engineers, Data Engineers, and Data Scientists. A basic understanding of Python and machine learning is a prerequisite. 

The LLM Twin course materials are available in this GitHub repo.

LLM University from Cohere

You can go through all the materials at your own pace.
Author: Cohere
Course certificate: no.
Price: free.
The course syllabus is available here.

Cohere’s LLM University is a free learning hub designed to help developers and technical professionals master LLMs and Generative AI. It offers hands-on modules covering key topics such as transformer architecture, embeddings, semantic search, prompt engineering, fine-tuning, and deploying LLMs in production environments. 

Practical modules also include step-by-step tutorials on building a RAG-based chatbot and a multi-turn AI agent. After completing this course, learners will be able to build, optimize, and deploy enterprise-grade AI applications.

Cohere’s LLM University materials are available on the hub website. 

Generative AI with LLMs from AWS

Author: DeepLearning.AI, AWS
Duration: 3 weeks
Course certificate: yes (requires a Coursera subscription).
Price: $49/month, but content can be accessed for free.
The course syllabus is available here.

The Generative AI with LLMs course from DeepLearning.AI and AWS teaches the fundamentals of how generative AI works and how to deploy it in real-world applications. It covers the entire LLM-based product lifecycle, from data collection and model selection to performance evaluation and deployment. The course explores the transformer architecture, training methods, fine-tuning techniques, and scaling strategies to optimize model performance within real-world constraints. 

Through applied techniques and case studies, the course gives the skills to apply generative AI in business contexts.

The course is available on Coursera and requires a subscription to earn a certificate of completion. A Coursera subscription currently costs $49 per month. However, if you only want access to the course materials, you can audit the course for free.

Generative AI with LLMs course videos are available on Coursera.  

Generative AI Engineering with LLMs from IBM

Author: IBM
Duration: 3 months
Course certificate: yes (requires a Coursera subscription).
Price: $49/month, but content can be accessed for free.
The course syllabus is available here.

Generative AI Engineering with LLMs specialization from IBM is designed for data scientists, ML engineers, and AI developers and is made up of 7 courses that cover: 

• Generative AI architectures and data preparation techniques
• How to use transformer-based LLMs and fine-tune them for specific tasks
• Fundamentals of AI agents and advanced RAG techniques

For the final assignment, the students are invited to build a RAG-based Q&A system.

The specialization is available on Coursera and requires a subscription to earn a certificate. However, if you only want to read and view the course content, you can audit the course for free.

Generative AI Engineering with LLMs specialization videos are available on Coursera.

[fs-toc-omit]Is your LLM app ready for the real world?

Sign up for our applied LLM evaluations course for AI builders. Learn how to create LLM judges, evaluate RAG systems, and run adversarial tests. Yes, it's free!

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