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.
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.