LLM and Language AI Development Courses
6 courses for developers working with language models — the systems behind text generation, semantic search, translation, and speech. Some focus on the application side, calling and orchestrating existing models and grounding them in your own data; others go deeper, into how a language model is built, trained, and tuned.
A large language model on its own is just an API that returns text, and a lot of the interesting work is everything around it. Several of these courses are for developers doing that work — connecting to language models, feeding them the right context, retrieving from your own documents so answers stay grounded, and giving a model tools so it can act rather than only answer. Others go the other way, under the hood: how text is represented, how a transformer processes it, and what it takes to train, fine-tune, or align a language model yourself. Between them you'll build real components — question-answering over your own documents, search that understands meaning rather than keywords, text and speech pipelines, and translation — and come away with a clear view of what the model underneath is actually doing.
LLM & Language AI Development courses
6 courses on the Develop AI track.
Building Language Models on AWS
AWS Skill Builder
Develop natural language solutions in Azure
Microsoft Learn
Get started with AI applications and agents on Azure
Microsoft Learn
Google DeepMind: AI Research Foundations
Google Skills
LLM University
Cohere
Rapid Application Development with Large Language Models (LLMs)
NVIDIA Deep Learning Institute
Frequently asked questions
- What will I be able to build after these language AI courses?
- Applications built on language models — question-answering grounded in your own documents, semantic search, and text or speech pipelines including translation — and, in the deeper courses here, the skills to train, fine-tune, and deploy a language model yourself.
- Do I need machine learning experience to build LLM applications?
- Not usually — many LLM application courses focus on using and orchestrating existing models through code rather than training your own, so strong programming skills matter more than deep machine learning theory; the courses here that go inside the model itself do expect more, so check the level noted on each course's page.
- Do these cover retrieval-augmented generation (RAG)?
- Yes — retrieval-augmented generation is the standard way to ground a language model in your own documents, so it appears among these courses; check each course's page to see whether it covers RAG in depth.
Key concepts
The foundational terms these courses build on — each chip links to a plain-English definition in the AI Pinnacle glossary.