Is Testing and Refining LLM Applications suitable for beginners with no software-testing experience?

This course assumes basic knowledge of Python and unit testing. It includes step‑by‑step labs for TDD and test automation; however, learners new to testing may want a short introduction to unit tests before starting.

What tools will I use in Testing and Refining LLM Applications?

You will use Python testing frameworks (unit tests and behavior test setups), mutation testing tools, DVC for data/model versioning, experiment tracking tools (e.g., W&B), and standard CLI scripting with argparse. CI/CD concepts and integration examples are included as well.

How does this course prepare my LLM service for production?

The course builds a repeatable engineering workflow: test-first development, safety and mutation testing to ensure guardrails, versioned datasets and tracked experiments to support model promotion, and automated scripts that fit within the CI/CD pipelines to prevent unsafe or untested deployments.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Testing and Refining LLM Applications

This course is part of LLM Engineering That Works: Prompting, Tuning, and Retrieval Professional Certificate

Instructor: Professionals from the Industry

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Apply TDD to microservice endpoints and refactor modules based on code reviews to improve readability and reduce complexity.
Develop behavior and safety tests to ensure LLM outputs comply with policies and block unsafe changes to the model.
Apply data versioning to track artifacts and evaluate ML experiment runs to select production-ready models.
Create scripts using Python's argparse to automate multi-step computational workflows in cloud environments.

Skills you'll gain

Tools you'll learn

Details to know

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This course is part of the LLM Engineering That Works: Prompting, Tuning, and Retrieval Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Coursera

There are 5 modules in this course

This course is designed for software engineers and ML practitioners aiming to advance from building LLM prototypes to deploying robust, production-grade AI systems. In the real world, a reliable application requires more than a clever prompt; it demands a rigorous software engineering foundation to ensure its testability, maintainability, and safety. This course provides that critical toolkit.

You will learn to apply Test-Driven Development (TDD) to methodically build and refactor LLM-powered microservices, ensuring that your code is clean and verifiable from day one. To safeguard your applications, you will create sophisticated behavioral test suites that enforce safety policies and prevent undesirable outputs. You'll go a step further by using mutation testing to evaluate the quality of your own tests, ensuring that your safety guardrails are truly effective. The course also dives into the MLOps lifecycle, teaching you to version datasets and models with DVC, track experiment results on platforms like W&B, and make data-driven decisions about the models to promote. Finally, you will learn to automate your entire testing and evaluation workflow using powerful Python scripts, thereby preparing your application for seamless integration into a CI/CD pipeline.

Module details

Rapid AI development often creates "technical debt," resulting in brittle, costly systems. This module shifts focus from basic scripts to professional software engineering for production-grade microservices. You will master Test-Driven Development (TDD), writing unit tests first to ensure reliability. The curriculum emphasizes code reviews and systematic refactoring, teaching you to transform monolithic code into clean, maintainable modules. Through hands-on VS Code labs, you will refactor legacy services and build new API endpoints, gaining the skills to deliver scalable, robust, and professional AI applications.

What's included

4 videos2 readings2 assignments2 ungraded labs

4 videosTotal 27 minutes

Preventing a $440 Million Mistake7 minutes
How to Build an Endpoint with TDD?7 minutes
Why "Clean" Code Matters?6 minutes
How to Refactor a Complex Function?7 minutes

2 readingsTotal 16 minutes

The Red-Green-Refactor Cycle of TDD8 minutes
A Practical Guide to Refactoring8 minutes

2 assignmentsTotal 35 minutes

Knowledge Check: TDD Principles5 minutes
Refactor and Extend a Microservice30 minutes

2 ungraded labsTotal 120 minutes

TDD in Action60 minutes
From Mess to Maintainable60 minutes

As AI models like Google's Gemini have shown, even the most advanced systems can have spectacular safety failures, leading to brand damage and a loss of user trust. This module teaches you the rigorous, adversarial testing methodologies that professional AI Red Teams use to secure high-stakes applications. By the end of this module, you will be able to not only ensure your LLM behaves safely but also prove that the tests verifying that safety are themselves comprehensive and robust.

What's included

4 videos2 readings3 assignments2 ungraded labs

4 videosTotal 27 minutes

When Good Models Go Bad: The Gemini Case Study?7 minutes
How to Build a Behavioral Test Suite?8 minutes
Why 100% Coverage Isn't Enough?6 minutes
How to Find and Kill Surviving Mutants?7 minutes

2 readingsTotal 20 minutes

What is Behavioral Safety Testing?10 minutes
What is Mutation Testing?10 minutes

3 assignmentsTotal 40 minutes

Knowledge Check: Behavioral Safety Testing5 minutes
Knowledge Check: Interpreting a Mutation Report5 minutes
Safety Risk Assessment: Mutation Testing in Financial LLMs30 minutes

2 ungraded labsTotal 120 minutes

Apply: Build Your First Safety Test Suite60 minutes
Apply: Harden Your Test Suite with Mutation Testing60 minutes

If you have ever faced the "it worked on my machine" problem or struggled to reproduce a great result from weeks ago, this course will provide you with the foundational MLOps practices to build a truly auditable and collaborative workflow. The primary goal is to empower you to manage the entire experiment lifecycle with confidence, ensuring that every model you build is reproducible, traceable, and ready for the rigors of production. For learners interested in applying these MLOps skills to the next frontier, this module serves as a perfect foundation for more advanced topics.

What's included

5 videos3 readings6 assignments1 ungraded lab

5 videosTotal 33 minutes

The "It Worked on My Machine" Problem 7 minutes
Your First DVC Snapshot: Step-by-Step7 minutes
From Spreadsheet Chaos to Organized Insights5 minutes
Instrumenting Your Training Script with W&B6 minutes
A Framework for Defensible Model Selection 6 minutes

3 readingsTotal 25 minutes

Introducing DVC: Git for Data10 minutes
The Anatomy of a Tracked Experiment 8 minutes
When the "Best" Model Isn't the Right One7 minutes

6 assignmentsTotal 100 minutes

Troubleshooting a Versioning Conflict 15 minutes
Hands-On Learning: Log Your First Experiment to W&B20 minutes
Spot the Bug: Debugging a W&B Script 10 minutes
Hands-On Learning: Model Evaluation for Content Moderation15 minutes
Auto-Graded Quiz: Making a Defensible Model Choice 10 minutes
ML Experiment Tracking & Evaluation Toolkit30 minutes

1 ungraded labTotal 20 minutes

Version a Dataset with DVC20 minutes

Modern ML workflows often involve multiple complex steps—provisioning a GPU, running a training job, and saving the model—all of which are inefficient to perform by hand. This module teaches you how to automate this entire process from end to end using Python. By the end, you will be equipped to transform your manual cloud processes into robust, automated pipelines ready for production.

What's included

3 videos2 readings2 assignments1 ungraded lab

3 videosTotal 22 minutes

Anatomy of an Automated Workflow Script8 minutes
Parsing and Using Arguments6 minutes
The Imperative for Resilient Automation9 minutes

2 readingsTotal 13 minutes

How-To: A Pocket Guide to Cloud SDKs5 minutes
Core techniques for Refactoring for Resilience8 minutes

2 assignmentsTotal 35 minutes

Knowledge Check: Automate Cloud Workflows5 minutes
Automate a Multi-Step Cloud Workflow30 minutes

1 ungraded labTotal 15 minutes

Express Python CLI Scripting15 minutes

In this module, you will take on the role of an engineer responsible for ensuring an AI-powered summarization microservice is safe and reliable. Through a hands-on project, you’ll use Python and pytest to build a comprehensive test suite that validates functionality and enforces safety policies. You will write unit tests to confirm the API’s core behavior and then develop critical behavioral tests to ensure the service refuses to generate harmful, illicit, or otherwise non-compliant content. This module will equip you with the practical skills to assert safety refusals, document your test strategy, and integrate your work into a CI pipeline to prevent unsafe code from ever reaching production.