Generative AI with LLMs: the DeepLearning.AI and AWS course that walks you through the full lifecycle of an LLM application

In June 2023, DeepLearning.AI and AWS jointly launched a hands-on course on large language models. Three years on, its most valuable part is not any particular technique but a map: the lifecycle of a generative AI project, from choosing a use case to putting a model into a product.

For anyone who wants to work as an FDE, that map is a daily tool. Sitting in front of a client, you have to answer a chain of questions quickly: does this problem need an LLM, which model should we use, do we need to fine-tune, how do we measure quality, how do we deploy?

The Generative AI with Large Language Models course is built around exactly that chain of questions.

Instructors from the deployment side

The course content was developed under the guidance of Andrew Ng, founder of DeepLearning.AI. It is taught by four AWS practitioners: Antje Barth, Chris Fregly, Shelbee Eigenbrode and Mike Chambers. The official page presents them as people who build and deploy AI for enterprise use cases themselves.

Aspiring FDEs should note this. Instructors who do deployment work tend to focus on “how do we make it work” rather than “why does it work”. AWS describes this as the first comprehensive Coursera course on LLMs to go into detail on the typical lifecycle of a generative AI project.

Each part of the course trains an FDE skill

The official page describes the course as built around the key steps of the lifecycle: data gathering, model selection, performance evaluation and deployment. Set the syllabus beside an FDE’s job and each part maps onto a specific skill.

Section	Content	FDE skill trained
Week 1	Generative AI use cases, project lifecycle, model pre-training	Scoping the problem; explaining to clients what LLMs can and cannot do
Weeks 2–3	Fine-tuning and evaluation, reinforcement learning, applications built on LLMs	Deciding when a model needs adapting, measuring quality, fitting the model into a real system

The hands-on work runs in an AWS environment hosted by partner Vocareum, so you apply techniques on real cloud infrastructure rather than just watching videos. Lab 2 asks you to fine-tune a generative AI model to summarise dialogue, a problem very close to what businesses actually ask for.

Three ideas worth keeping after the course

First: think in terms of the lifecycle before thinking about tools. Clients often arrive with a request that already has a model name attached. A good FDE pulls the conversation back to the first step, what the use case is, and only then moves on to data and models.

Second: fine-tuning and evaluation belong together. The course puts the two in the same block of content, and that is the right lesson: if you fine-tune without measuring before and after, you cannot prove to the client what you have improved.

Third: deployment is a step in the lifecycle, not an afterthought once the model is good. An excellent model that sits outside users’ workflow is close to useless, and this is often where an FDE creates the most value.

Five questions for a client meeting

Picture a retail chain opening a meeting by saying it wants to bring an LLM into its customer service department. The request is too broad to act on. The lifecycle map gives you five questions to narrow it down.

The first belongs to the use case step: who will use the output, and where are they losing time today? Suppose the answer is that shift managers have to read hundreds of chats a day to keep track of complaints. The problem has now shrunk to dialogue summarisation.

The second concerns data: how many chats are there, have they been anonymised, and are there any human-written summaries to serve as examples? The third concerns model selection: what are the constraints on latency, cost and where the data may be processed? These two questions rule out options before anyone has opened a notebook.

The fourth decides whether to fine-tune: what does a good summary look like, and who will judge it? If ordinary prompting already meets the client’s bar, you save a whole round of adaptation; if not, that is the moment for the Lab 2 technique, with measurements before and after.

The last belongs to the deployment step: where will the summary appear in the tools staff use every day? After five questions, you leave the meeting with a measurable problem instead of a vague wish.

Who should take it, and in what order

The course is aimed at data scientists and engineers who want to build generative AI applications with LLMs. It is an intermediate course: the official page says you need experience writing Python, plus basic ML fundamentals.

If you are a backend or full-stack developer who has never touched ML, do not jump straight in. Shore up your Python and basic ML concepts first, then take the course, and build a small project of your own as soon as you finish. If you already know ML, you can move quickly through the pre-training material and spend your time on the labs.

Be clear-eyed about timing, too. The course launched in 2023, so treat it as a foundation for lifecycle thinking and find newer material yourself for techniques that have appeared since.

Turn the certificate into evidence

A certificate is usually less persuasive than a working project. What you need to show an interviewer is the ability to take a vague problem all the way to a functioning system. So do not leave the labs sitting in the learning environment.

Take the Lab 2 idea, apply it to a public dialogue dataset or anonymised data from your work, and write a README that follows the lifecycle: use case, data, why you chose the model, how you evaluated it, how you deployed it. On a CV, a project line described in that order says more than the course title.

When reading FDE job descriptions, look for phrases such as “end-to-end”, “from prototype to production” or “working directly with customers”. That is where the lifecycle map you learned here starts to pay off.