Foundation Model Applications

Before you work through any course, it helps to know how the course is built and what you are expected to do as you move through it. This lesson is an orientation. Its job is not to teach a new subject in depth. Its job is to give you a reliable picture of the course so that the later lessons make sense when you reach them.

Many learners skip orientation material because it feels like a delay before the real content begins. That instinct is understandable, but it tends to cost time later. A learner who does not know how the lessons are grouped may wonder whether a missing step was skipped by accident. A learner who does not know what each lesson is responsible for may look for answers in the wrong place. Keep that in mind as you trace the structure below, because each part of the course has a specific role.

The course is organized into three nested levels. At the top is the module. Each module is divided into chapters. Chapters hold the individual lessons, and each lesson focuses on one topic. Reading the course from the top down, you move from a module, to a chapter inside it, to a lesson inside that chapter.

The diagram shows the path to this lesson. The course contains modules, a module contains chapters, and a chapter contains lessons. The diagram is simplified: the source describes later modules without listing every chapter inside them, so the later branch is shown only as a single box.

Lessons are not isolated. Each lesson is meant to build on the lessons before it in the same chapter, and chapters within a module usually build on each other as well. This is why the course is best treated as a sequence rather than a reference collection. Jumping ahead to a later lesson without the earlier ones leaves gaps that the lesson itself is not designed to fill.

The next exercise follows a learner's position down the hierarchy. The example below is made up for illustration. It shows how the parts of the course fit together in practice. It is not a required procedure.

The course begins with the Getting Started module. Later modules move into broader material. According to the source, these include the evolution of AI systems, the design of applications built on foundation models, the evaluation of model performance, and the engineering of retrieval, prompting, finetuning, data preparation, and inference. The order below reflects the sequence in which the source introduces them.

You do not need to understand any of those later topics yet. You only need to know that they come after the foundations that Getting Started provides. Knowing this order lets you recognize when a topic belongs to a later stage, so that you do not expect a full explanation of it here.

This lesson has a deliberately narrow scope. It is useful to know what falls inside that scope, because a missing explanation here is usually intentional rather than an omission.

The source also notes a parent-level idea that the course returns to. With traditional ML engineering, model quality is a differentiator because teams build with proprietary models. With foundation models, many teams use the same model, so differentiation must come from the application development process. The source names the responsibilities of that application development layer as evaluation, prompt engineering, and the AI interface. This lesson introduces the idea only as a preview. The detailed treatment belongs to later lessons.

A common assumption is that every lesson requires the same environment, software, or tools. The source says otherwise. Whether a lesson needs a particular environment is introduced when that lesson needs it. If a later lesson depends on something you must prepare, that lesson is where the requirement should be stated, and you should follow its instructions rather than assuming them in advance.

The practical lesson is that you should not prepare tools in advance based on this orientation alone. Read each lesson's opening section for its purpose, and wait for the lesson to tell you what it requires.