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⇱ Advanced Prompting & AI Tooling | Coursera

Advanced Prompting & AI Tooling

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Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Master advanced techniques in prompt engineering, including "Flip the Script" and self-consistency.

  • Develop AI-powered tools like code reviewers using Git and advanced error handling techniques.

  • Implement function calling and self-critique workflows to refine AI output.

  • Design structured outputs and manage data effectively for complex AI applications.

Details to know

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Recently updated!

March 2026

Assessments

5 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Prompt Engineering Masterclass - From Beginner to Advanced Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 3 modules in this course

This course features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this advanced course, you'll deepen your expertise in prompt engineering and learn how to craft highly effective prompts for sophisticated AI models. The course covers a range of advanced techniques, such as the "Flip the Script" pattern, self-consistency, function calling, and more. With practical labs, you’ll experiment with these techniques, refining AI-generated prompts, and building more dynamic, flexible, and high-performing AI systems. You'll also dive into function calling and applying it to real-world tasks, as well as improving response quality through decomposition and self-critique. The course also includes a comprehensive project where you will build an AI-powered code reviewer, allowing you to apply your prompt engineering skills in a practical setting. Throughout the project, you’ll enhance the tool with features like Git integration, code logic and syntax checking, self-critique, and the creation of expert personas. The project will culminate with the migration to structured output, improving the tool’s data management and its interaction with other systems. This course is ideal for learners who have a solid understanding of AI models and prompt engineering, and wish to take their skills to the next level by designing more powerful, efficient, and customized AI-driven tools. The course requires experience in programming and basic familiarity with AI principles. By the end of the course, you will be able to build sophisticated AI-powered tools, refine and optimize prompts for complex tasks, and integrate advanced techniques like function calling and self-consistency into your AI systems.

In this module, we will explore advanced prompt engineering techniques designed to optimize model behavior. You'll learn how to apply dynamic patterns like "Flip the Script," use function calling within prompts, and enhance responses using self-consistency. Practical labs will allow you to apply these concepts in real-time to refine your skills.

What's included

11 videos2 readings1 assignment

11 videos•Total 103 minutes
  • Section Overview•3 minutes
  • Practical Lab: The "Flip the Script" Pattern•11 minutes
  • Practical Lab: "Flip the Script" Pattern Applications•7 minutes
  • Practical Lab: Using AI to Generate Prompts•7 minutes
  • Practical Lab: Refining AI-Generated Prompts•16 minutes
  • Practical Lab: Breaking Down Complex Tasks with Decomposition•16 minutes
  • Practical Lab: Improving Responses with Self-Critique•11 minutes
  • Practical Lab: Introduction to Function Calling•11 minutes
  • Practical Lab: Advanced Function Calling•7 minutes
  • Practical Lab: Introduction to Self-Consistency•7 minutes
  • Practical Lab: Self-Consistency Wrap-Up•6 minutes
2 readings•Total 20 minutes
  • Introduction to the Course 'Advanced Prompting & AI Tooling'•10 minutes
  • Full Specialization Resources•10 minutes
1 assignment•Total 15 minutes
  • Mastering Advanced Prompt Engineering - Assessment•15 minutes

In this module, we will work on building an AI-powered code reviewer. You’ll learn how to use the GitPython library for code interaction, develop expert personas for deeper analysis, and refine your tool’s logic with self-consistency techniques. This module provides a comprehensive approach to creating a tool that can review code effectively.

What's included

20 videos1 assignment

20 videos•Total 194 minutes
  • Module Overview and Goals•1 minute
  • Reviewing the Module Implementation Plan•4 minutes
  • Refactoring to Use the GitPython Library•18 minutes
  • Improving Exception Handling•4 minutes
  • Creating the Boilerplate for the Review Command•6 minutes
  • Using Dataclasses for Structured Data•14 minutes
  • Adding the Review Command to the CLI•5 minutes
  • Designing Prompts for Logic and Syntax Checks•10 minutes
  • Executing the Core AI Review Logic•12 minutes
  • Developing Expert Personas for Deeper Code Analysis•9 minutes
  • Building a Self-Consistency Workflow for Reviews•14 minutes
  • Completing the Self-Consistency Implementation•7 minutes
  • Defining External Tools for the Reviewer•16 minutes
  • Building a Basic Tool Registry•14 minutes
  • Completing the Tool Registry•10 minutes
  • Resolving Static Typing Errors•10 minutes
  • Creating the Initial AI Tool-Calling Loop•7 minutes
  • Refining the Tool-Calling Logic•12 minutes
  • Writing Tests for the Tool-Calling Feature•9 minutes
  • Integrating a Self-Critique Phase into the Review Process•14 minutes
1 assignment•Total 15 minutes
  • Project Module #3: Building an AI Code Reviewer - Assessment•15 minutes

In this final module, we will guide you through the process of structuring the output of your AI-powered code reviewer, ensuring better data management. You'll refactor your tool for maintainability, fix any remaining bugs, and complete the project by adding final touches, such as building a JSON output parser and documenting the tool for future users.

What's included

13 videos1 reading3 assignments

13 videos•Total 130 minutes
  • Module Overview and Goals•1 minute
  • Initiating the Migration to Structured Output•15 minutes
  • Refactoring the Review Module for Maintainability•17 minutes
  • Resolving Test Failures After Refactoring•9 minutes
  • Updating Prompts to Generate JSON Output•13 minutes
  • Modifying Tests to Validate JSON Output•5 minutes
  • Adapting the Pipeline to Use Dataclasses•13 minutes
  • Continuing the Dataclass Migration•7 minutes
  • Finalizing the Pipeline Migration•14 minutes
  • Addressing and Fixing Minor Bugs•6 minutes
  • Correcting Remaining Test Failures•7 minutes
  • Building the JSON Output Parser•10 minutes
  • Final Touches: Synthesis Logic and Documentation•13 minutes
1 reading•Total 10 minutes
  • Conclusion to the Course 'Advanced Prompting & AI Tooling'•10 minutes
3 assignments•Total 90 minutes
  • Project Module #4: Structured Output and Finalization - Assessment•15 minutes
  • Full Course Assessment•60 minutes
  • Full Course Practice Assessment•15 minutes

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Frequently asked questions

Prompt Engineering involves designing the inputs, or "prompts," given to large language models (LLMs) in order to guide their outputs. This skill is essential because it allows users to enhance the performance and effectiveness of AI models in tasks such as text generation, summarization, and problem-solving. As AI becomes increasingly integrated into various applications, prompt engineering helps to ensure that these tools are used efficiently and produce high-quality results.

The "Prompt Engineering Masterclass - From Beginner to Advanced" course covers both foundational and advanced techniques for working with large language models. You'll start by learning the basics of prompt engineering and gradually progress to more advanced strategies, such as the "Flip the Script" pattern, function calling, and self-consistency techniques. The course also includes practical labs where you’ll apply these concepts to real-world projects, including building an AI-powered code reviewer and generating structured outputs for your models.

After completing this course, you will be able to design effective prompts for large language models, fine-tune model behavior, and build advanced AI tools like an AI-powered code reviewer. You’ll also be skilled in advanced techniques like using personas, refining AI-generated prompts, and integrating self-critique phases into your workflows. This will prepare you to develop complex AI-powered applications and optimize model outputs for various use cases.

This course is designed for learners with a basic understanding of programming, particularly in Python. Some familiarity with APIs and working with AI models will be helpful, but not mandatory. The course covers all the foundational concepts required to interact with large language models, so you don't need prior experience in prompt engineering or advanced AI techniques. An interest in building AI-powered tools and applications is key to succeeding in the course.

This course is ideal for developers, data scientists, and AI enthusiasts who want to deepen their understanding of prompt engineering and gain hands-on experience in building AI applications. It’s also perfect for those who wish to refine their skills in working with large language models, whether for research, personal projects, or professional work. If you are looking to advance your ability to interact with AI tools and optimize model outputs, this course is for you.

The course is designed to be completed in 11 hours. This includes video lectures, hands-on labs, and project-based learning, where you’ll work on building an AI-powered tool from start to finish. You can complete the course in a single day or spread it out over several sessions, depending on your pace.

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when available.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,