Google Introduction to Large Language Models: Free LLM Course

AI assistants can produce remarkably fluent text, but choosing how to use them starts with understanding the models underneath. Google’s Introduction to Large Language Models offers a free, focused overview of LLMs, their applications and the role of prompt tuning.

The one-hour microlearning course is a useful starting point for students, product teams and other learners who want to follow discussions about language-model applications. Its scope is conceptual: one video, accompanying reading and a quiz, rather than a software-building project.

Course at a glance

What you’ll learn

• Explain what an LLM is: build a starting vocabulary for discussing large language models.
• Recognise potential applications: connect the technology with language-related use cases.
• Understand prompt tuning: explore how adapting a model’s prompt-related inputs can help it address a task.
• Identify Google development tools: learn about the tools introduced for generative AI applications.

Skills you’ll gain

• LLM terminology
• Language-model use-case analysis
• Prompt-tuning concepts
• Generative AI tool awareness
• Application scoping

Understand the model before choosing the application

Large language models contain many learned parameters and can support applications that work with language. A chatbot is one application of a model. Keeping those ideas separate helps you look beyond the chat window you have seen.

For example, a team considering a writing assistant should specify the writing task, the information available and the expected output. A first draft of a welcome message and a fact-sensitive summary call for different evaluation criteria. This course gives you a starting framework for discussing use cases; it does not establish that a particular model will perform them reliably.

Prompt tuning and prompt writing are different

One valuable topic here is prompt tuning. In machine learning, that term refers to learning task-specific prompt-related inputs, such as a soft prompt, while keeping the main model’s parameters fixed. It is different from a person editing the wording of an instruction in a chat box.

Prompt design is the process of writing inputs that guide a response. Tuning involves a learning process. You do not need to perform either technique in a paid service to follow this introductory course, and the listed activities include no tuning lab.

When taking notes, keep separate entries for the model, the user’s instruction and any task-specific adaptation. That simple habit can make later documentation much easier to follow. If the vocabulary is unfamiliar, start with the broad idea and return to the technical detail after the video.

A useful first application brief

As an optional personal exercise, outline an assistant that rewrites short event announcements for different audiences. Describe the input, the intended reader and the desired length. Then write two checks: which facts must stay unchanged, and which aspects of style can vary?

Keep the brief to one page and ask how you would check that an important date survives a rewrite. This optional exercise needs only paper or a text editor, with no API calls.

Use the course to improve the brief’s terminology and state the problem clearly. Building a working assistant would require further learning and implementation.

Access and completion

The current course is advertised as Free. Its complete listed route consists of a video, reading and a quiz, with no hands-on lab. Sign in with a free Google Skills account to study, track progress and earn the advertised completion badge after meeting the required activities.

No subscription, card, trial, Cloud deployment or paid API use is required for this route. Google development tools are discussed as learning material. For your next step, browse the free course catalogue for a topic that matches your goals.

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Frequently asked questions