This course provides an in-depth exploration of Large Language Models (LLMs) using the Hugging Face ecosystem. Participants will learn to deploy their first model and master essential skills such as prompt engineering and system design. The course covers techniques to control LLM outputs, manage conversations effectively, and create a vector knowledge base, including knowledge-augmented LLMs. Additionally, learners will implement the Retrieval-Augmented Generation (RAG) API, extend LLMs with tools and function calling, and develop agentic LLM systems using reusable patterns. Finally, the course will delve into Hugging Face inferencing and pricing models, equipping learners with the knowledge to leverage LLMs in real-world applications.
Large Language Models with Hugging Face
Gain insight into a topic and learn the fundamentals.
4 hours to complete
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
February 2026
Assessments
4 assignments
Taught in English
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There are 4 modules in this course
Explore the foundational concepts of interacting with large language models using Hugging Face. Learn to navigate the Hugging Face Hub, deploy models locally, and master prompt engineering techniques for real-world applications.
What's included
19 videos1 reading1 assignment
Focus on enhancing LLM capabilities with knowledge augmentation and tool integration. Create vector knowledge bases, implement retrieval-augmented generation, and extend LLMs with practical tools.
What's included
16 videos1 reading1 assignment
Explore the creation of agentic systems and deployment strategies. Learn about agentic LLM systems, Hugging Face inferencing, and pricing models for effective deployment.
What's included
11 videos1 reading1 assignment
Apply all course concepts to build a production-ready AI-powered research assistant combining RAG, agents, and API development.
What's included
1 video1 reading1 assignment
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