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10 Best Prompt Engineering Courses (July 2025)

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In the artificial intelligence field, the art of prompt engineering has emerged as a pivotal skill set for professionals and enthusiasts alike. As AI systems, particularly language models like GPT, become increasingly sophisticated, the ability to effectively communicate with these models has gained paramount importance. Prompt engineering, essentially, is the craft of designing inputs that guide these AI systems to produce the most accurate, relevant, and creative outputs.

But what exactly is prompt engineering, and why has it become such a buzzword in the tech community?

Prompt engineering is the art and science of crafting inputs (or “prompts”) to effectively guide and interact with generative AI models, particularly large language models (LLMs) like ChatGPT. It involves formulating questions or statements in a way that leverages the capabilities of AI to produce specific, relevant, and accurate outputs.

This field sits at the intersection of linguistics, computer science, and creative thinking. It requires an understanding of how AI models process information and a creative touch to tailor prompts that align with the desired outcome. The goal is to maximize the efficiency and applicability of AI responses, whether it’s generating text, code, images, or even engaging in complex problem-solving.

In recognition of this burgeoning field, we have curated a list of the best prompt engineering courses. Whether you are a seasoned AI practitioner or a curious newcomer, these courses offer invaluable insights into the nuances of prompt engineering. From understanding the basic principles of language models to mastering advanced techniques for specific applications, these courses cover a wide spectrum of knowledge and skills.

Google’s “Prompting Essentials” course on Coursera is designed to teach the fundamentals of generative AI prompting through a straightforward five-step framework. Created by Google Career Certificates, this beginner-friendly course guides users in writing clear and effective AI prompts, allowing learners to complete real-world tasks more efficiently. Spanning under 10 hours, it provides hands-on exercises where participants practice prompt design, evaluation, and AI iteration. From crafting targeted emails and summarizing lengthy documents to building trackers and brainstorming fresh ideas, learners apply prompting techniques across a variety of workplace tasks, enhancing productivity with customized, reusable prompts.

The course goes beyond basic prompting to help learners analyze data, create compelling visualizations, and build presentations with AI feedback. Additionally, learners are introduced to advanced techniques such as prompt chaining and multimodal prompting, turning abstract ideas into practical steps. Emphasizing responsible AI use, the course teaches bias recognition and output evaluation, ensuring prompts are accurate and fair. By the end, learners are equipped with a certificate from Google and a library of prompts, empowering them to make AI a valuable tool in their professional toolkit.

Key Features

  • Course Overview: Google’s “Prompting Essentials” teaches AI prompting fundamentals with a simple five-step method.
  • Beginner-Friendly: Ideal for newcomers, the course is under 10 hours with practical exercises.
  • Workplace Tasks: Learn prompts for emails, document summaries, trackers, and brainstorming.
  • Reusable Prompts: Build a prompt library for efficient AI use across tools.
  • Advanced Skills: Master prompt chaining and multimodal techniques for complex ideas.
  • Data & Presentations: Analyze data, create visuals, and get AI feedback on presentations.
  • Bias & Accuracy: Learn to recognize AI bias and evaluate output quality.
  • Certificate: Earn a Google Career Certificate to boost your AI skills profile.

The “Prompt Engineering Specialization” offered by Vanderbilt University is an exceptional program designed to transform learners into experts in prompt engineering. This comprehensive series of three courses takes you from the fundamentals to advanced techniques, enabling you to harness the full potential of Generative AI. The specialization is centered around practical, hands-on learning, ensuring that students don’t just understand the theories but can effectively apply them in various contexts.

The specialization starts with the “Prompt Engineering for ChatGPT” course, which spans 18 hours and has an impressive rating of 4.8 from over 2,000 participants. Here, learners delve into the art of crafting prompts for large language models like ChatGPT, learning how to leverage their capabilities for a range of applications. The second course, “ChatGPT Advanced Data Analysis,” focuses on automating tasks using ChatGPT’s code interpreter. This 10-hour course, also highly rated at 4.8, teaches students to automate document handling and data extraction, among other skills. The final course, “Trustworthy Generative AI,” is an 8-hour journey into ensuring reliability and trust in AI outputs.

Key Features

  • In-Depth Learning: From basic concepts to advanced skills in prompt engineering.
  • Hands-On Projects: Practical exercises to build and refine your prompt engineering skills.
  • Diverse Applications: Learn to apply these skills in various contexts like automation, data analysis, and problem-solving.
  • Applied Learning Project: Real-world applications, from social media posts to complex problem-solving using AI.
  • Expert Instruction: Learn from university and industry experts in the field.
  • Certification: Earn a career-enhancing certificate from Vanderbilt University.

This course is ideal for individuals looking to deeply understand and effectively apply prompt engineering in their professional, academic, or personal endeavors.

“Generative AI: Prompt Engineering Basics” offered by IBM is an essential course for beginners looking to establish a strong foundation in the field of prompt engineering. This 7-hour course is tailored to provide a flexible learning experience, perfect for professionals, students, and enthusiasts who are eager to explore the world of generative AI. The course is structured to impart a comprehensive understanding of prompt engineering concepts, best practices, and practical techniques.

The course is divided into three well-structured modules, each focusing on different aspects of prompt engineering. Module 1 introduces the concept of prompt engineering in generative AI, emphasizing best practices for writing effective prompts. Module 2 delves deeper into specific techniques and approaches to enhance the precision and relevance of generative AI model responses. The final module includes a graded quiz, a hands-on project, and optional content that extends into image generation prompts and the use of IBM’s Prompt Lab tool.

Key Features

  • Beginner-Friendly: Designed for a wide audience, including professionals and enthusiasts with no prior experience.
  • Flexible Learning: Learn at your own pace with a course structure that accommodates different schedules.
  • Comprehensive Modules: Covering fundamental concepts, techniques, and hands-on practice in prompt engineering.
  • Diverse Techniques: Explore zero-shot, few-shot, Interview Pattern, Chain-of-Thought, and Tree-of-Thought techniques.
  • Industry Tools: Introduction to tools like IBM Watsonx Prompt Lab, Spellbook, and Dust for practical prompt engineering.
  • Hands-On Experience: Engage in labs and a final project to apply the learned concepts in real-world scenarios.
  • Industry Expert Insights: Gain insights from practitioners about effective prompt crafting and tool utilization.
  • Career Certificate: Earn a shareable certificate upon completion, adding value to your professional profile.

This course is an excellent choice for those starting their journey in prompt engineering, offering a solid grounding in the field and equipping learners with the skills to effectively guide AI models towards desired outcomes.

Edureka’s “Prompt Engineering with Generative AI” course is an expertly crafted program by leading industry professionals, designed for individuals keen on delving into AI-driven creativity and practical applications of prompt engineering. This comprehensive course is tailored to empower learners with the skills to effectively utilize prompts for generating customized text, code, and more, transforming the landscape of problem-solving. It’s an ideal platform for anyone aiming to be a pioneer in the field of artificial intelligence innovation.

The course unfolds through five in-depth modules, each focusing on a distinct aspect of prompt engineering and generative AI. It begins with “Generative AI and its Industry Applications,” introducing the principles of Generative AI, various generative models, their applications, and ethical considerations. The journey continues with “NLP and Deep Learning,” diving into the essentials of Natural Language Processing, deep learning’s role in NLP, and foundational concepts of neural networks. The third module, “Autoencoders and GANs,” ventures into the realms of autoencoders and Generative Adversarial Networks, exploring their architecture, training, and diverse applications.

As the course progresses, “Language Models and Transformer-based Generative Models” take center stage, shedding light on different language models, the Transformer architecture, and advanced models like GPT and BERT. The culminating module, “Prompt Engineering,” is dedicated entirely to the principles and practices of prompt engineering, encompassing prompt design strategies, types of prompting, and the art of crafting effective prompts.

Key Features

  • Expertly Curated Content: Developed by industry leaders to ensure relevance and practicality.
  • Wide-Ranging Curriculum: Encompasses everything from AI fundamentals to sophisticated prompt engineering techniques.
  • Focus on Practical Applications: Strong emphasis on using prompts for real-world problem-solving across various domains.
  • Detailed Modular Approach: Each module provides thorough insights into specific areas of generative AI and prompt engineering.
  • Up-to-Date Industry Topics: Includes the latest developments in AI models and their applications.
  • Hands-On Learning: Opportunities for practical application and experimentation with diverse AI tools and models.
  • Career Advancement: Ideal for professionals seeking to integrate advanced AI skills into their skillset.

This course is a perfect fit for those seeking a comprehensive understanding of generative AI and its practical applications in prompt engineering, offering valuable knowledge and skills for any AI professional or enthusiast.

The “ChatGPT Complete Course: Beginners to Advanced” is a comprehensive program designed for those eager to master generative AI with a focus on ChatGPT. This course covers prompt engineering, plugin integration, and ChatGPT API usage, and offers insights into the latest developments like GPT-4 and ChatGPT Plus. It’s tailored for learners who aim to excel in the ever-evolving digital tech space.

The course is structured into five key modules, each offering a unique perspective on ChatGPT and its applications:

  1. Unveiling ChatGPT covers the basics of Generative AI and ChatGPT, including its applications and the future of human-AI collaboration.
  2. Prompt Engineering and ChatGPT Plugins delves into prompt engineering fundamentals, its applications, and enhancing ChatGPT responses.
  3. ChatGPT for Productivity showcases the use of ChatGPT in various fields such as data science, marketing, and project management.
  4. ChatGPT for Developers focuses on programming, debugging, and API integrations with ChatGPT.
  5. GPT Models and Fine-tuning ChatGPT explores the architecture and fine-tuning of GPT models, including data preparation and training processes.

This course structure ensures a holistic learning experience, combining theoretical knowledge with practical applications.

Key Features

  • Comprehensive Curriculum: Covering ChatGPT from basics to advanced applications.
  • Practical Projects: Real-life case studies for hands-on learning.
  • Emphasis on Prompt Engineering: Focused on prompt engineering and ChatGPT API integrations.
  • Latest AI Advancements: Insights into developments like GPT-4 and ChatGPT Plus.
  • Versatile Toolset Exposure: Including Python, Java, TensorFlow, and Keras.
  • Career Enhancement: Ideal for individuals aiming to enhance their skills in AI and digital technology.

This course is a valuable resource for anyone from beginners to advanced learners seeking to delve into the world of ChatGPT and its diverse applications.

“Prompt Engineering for ChatGPT” by Vanderbilt University is a course that equips learners with the expertise to work effectively with large language models like ChatGPT. Part of the Prompt Engineering Specialization program, it is designed to demonstrate the transformative power of prompt engineering in various aspects of life and business; this course is perfect for anyone aspiring to master the use of generative AI tools.

The course is structured into six modules, focusing on the significance of ChatGPT and similar large language models in a range of applications. It begins by emphasizing the importance of understanding how these models respond to natural language prompts. The curriculum progresses from basic prompt crafting to sophisticated techniques, aimed at solving complex problems across different domains.

Learners will explore the vast potential of ChatGPT in tasks like writing, summarization, game play, planning, simulation, and programming. The course is designed to build strong prompt engineering skills, making students proficient in using large language models for diverse tasks in their jobs, businesses, and personal lives.

Key Features

  • Comprehensive Curriculum: Focused on effective prompt engineering for large language models.
  • Practical Application: Showcases diverse uses of ChatGPT in personal and professional contexts.
  • Skill Development: Emphasizes building strong prompt writing skills for enhanced productivity.
  • Broad Audience Appeal: Suitable for anyone with basic computer usage skills.
  • Problem-Solving Focus: Equips learners to tackle complex problems using prompt engineering.

This course is ideal for individuals looking to delve deep into the world of generative AI, offering a gateway to becoming proficient in the rapidly evolving field of prompt engineering.

“ChatGPT Prompt Engineering for Developers,” a course offered by DeepLearning.AI, is designed to immerse aspiring prompt engineers into the art of generating precise and engaging AI responses. Created by Isa Fulford from OpenAI and Andrew Ng from DeepLearning.AI, this course goes beyond the basics of prompt creation for web interfaces, focusing on leveraging LLMs through API calls for building generative AI applications.

This course stands out for its practical approach, incorporating real-world examples to provide a thorough understanding of prompt engineering. It starts with best practices in prompting software development, covering essential areas such as summarizing complex information, inferring from incomplete data, transforming text styles, and expanding ideas into detailed narratives using advanced machine learning techniques.

Additionally, the course delves into efficient management of prompt libraries, a crucial skill for prompt engineering roles. It caters to a wide range of learners, from beginners with a basic understanding of Python to advanced machine learning engineers seeking to explore the forefront of prompt engineering and the use of LLMs.

Key Features

  • In-Depth Learning: Offers a comprehensive guide on prompt engineering for developers.
  • Practical Approach: Emphasizes real-world applications and practical examples.
  • Diverse Skills Development: Covers summarizing, inferring, transforming, and expanding text using AI.
  • Prompt Library Management: Teaches efficient organization and utilization of prompt libraries.
  • Wide Audience Appeal: Suitable for beginners with basic Python knowledge and advanced engineers.
  • Expert Creators: Developed by renowned professionals from OpenAI and DeepLearning.AI.

This course is ideal for anyone aiming to master prompt engineering, whether starting out or looking to advance in the field, providing essential skills for leveraging AI in software development.

LearnPrompting’s “Introductory Course on Prompt Engineering” offers an ideal entry point into the world of AI prompt engineering. This free course is tailored for both beginners and advanced learners, providing a comprehensive overview of AI concepts and intricate prompt engineering techniques. Renowned for its high-quality content and effectiveness, the course is a foundational guide in the complexities of generative artificial intelligence, crucial in the modern landscape of computer science.

The course is thoughtfully structured, starting with an introduction to AI systems and their applications, before delving into the basics of prompt engineering. It addresses how input prompts function within language models like ChatGPT. The in-depth learning modules further explore topics like neural networks and machine learning techniques, making complex subjects accessible and understandable.

A standout feature of this course is its hands-on approach, offering students practical experience with real-world generative AI applications. This not only enhances theoretical knowledge but also equips learners with practical skills applicable to prompt engineering roles or similar positions involving AI models.

The course covers a range of topics including prompting, role prompting, few-shot prompting, combining techniques, formalizing prompts, chatbot basics, LLM settings, and the pitfalls of LLMs. Visual aids and examples accompany each concept, ensuring an engaging and contextual learning experience.

Key Features

  • Comprehensive Coverage: From basic AI concepts to advanced prompt engineering techniques.
  • Accessible Learning: Breaks down complex topics into easily understandable modules.
  • Practical Experience: Hands-on work with real-world AI applications.
  • Diverse Topics: In-depth exploration of various aspects of prompt engineering.
  • Engaging Teaching Methods: Use of visual aids and examples to enhance understanding.
  • Free Access: Making quality education in AI accessible to everyone.

This introductory course by LearnPrompting is perfect for anyone looking to step into the world of AI prompt engineering, offering a solid foundation and practical skills that are crucial for success in this field.

“Building Systems with the ChatGPT API” is a specialized course designed to teach efficient construction of multi-step systems using large language models. This course is ideal for learners looking to automate complex workflows and unlock new development capabilities. Taught by Isa Fulford from OpenAI and Andrew Ng from DeepLearning.AI, it builds on their popular “ChatGPT Prompt Engineering for Developers” and gives insights into creating sophisticated systems that interact dynamically with AI models.

The course is concise yet packed with practical knowledge. It centers around the concept of using multistage prompts to split complex tasks into a pipeline of subtasks, enhancing the efficiency and functionality of LLMs. Key learning points include:

  • Developing chains of prompts that build upon the completions of prior prompts.
  • Creating systems where Python code interacts with both AI completions and new prompts.
  • Building a customer service chatbot using all the techniques covered in the course.

Learners will also gain skills in classifying user queries, evaluating queries for safety, and processing tasks for chain-of-thought, multi-step reasoning. Despite being a one-hour course, it provides a comprehensive understanding of practical applications, including hands-on examples and built-in Jupyter notebooks for experimenting with the concepts taught.

Key Features

  • Efficient System Building: Learn to construct multi-step systems using LLMs.
  • Practical Learning Approach: Hands-on examples for easy comprehension of concepts.
  • Expert Instruction: Taught by industry experts Isa Fulford and Andrew Ng.
  • Interactive Learning Tools: Built-in Jupyter notebooks for practical experimentation.
  • Advanced Techniques: Focus on multistage prompts and Python code interactions.
  • Real-World Applications: Skills applicable to customer service chatbots and safety evaluations.

This course is perfect for individuals who have some familiarity with AI and are looking to enhance their skills in building advanced, AI-driven systems. Whether you’ve completed the “ChatGPT Prompt Engineering for Developers” course or are just starting, this course offers valuable insights into the practical applications of the ChatGPT API.

The “Generative AI Fundamentals Specialization” by IBM is a comprehensive program designed to impart a deep understanding of generative AI’s fundamental concepts, models, tools, and applications. This specialization is ideal for anyone looking to leverage the potential of generative AI in enhancing their workplace, career, and overall life. It’s suitable for professionals from all fields and does not require prior technical knowledge or a background in AI.

The specialization consists of five short, self-paced courses, each taking approximately 3–5 hours to complete. These courses cover a range of topics, including:

  1. Fundamental concepts and capabilities of generative AI foundation models.
  2. Powerful prompt engineering techniques to write effective prompts for desired AI outcomes.
  3. Building blocks and foundation models of generative AI, like GPT, DALL-E, and IBM Granite.
  4. Ethical implications and considerations of generative AI.
  5. Practical applications of generative AI to boost careers and productivity.

The specialization also includes hands-on labs and projects, allowing learners to practice using popular tools and platforms such as IBM watsonx.ai, OpenAI ChatGPT, Stable Diffusion, and Hugging Face. These labs provide practical experience with text, image, and code generation, prompt engineering tools, and foundation models.

Key Features

  • Comprehensive Understanding: Gain a thorough insight into generative AI concepts, tools, and applications.
  • Practical Prompt Engineering: Learn to write effective prompts for generative AI models.
  • Ethical Awareness: Discuss the limitations and ethical considerations of generative AI.
  • Career Enhancement: Recognize the potential of generative AI to improve professional skills and workplace efficiency.
  • Hands-On Learning: Engage in labs and projects using popular AI tools and platforms.
  • Accessibility: Suitable for learners with no prior AI background, benefiting professionals from various fields.

This specialization is a valuable resource for anyone passionate about unlocking the capabilities of generative AI and applying them in a professional context. It offers practical knowledge and skills, making it an ideal learning path for those new to the field or looking to enhance their understanding of generative AI technologies.

Navigating AI Through Prompt Engineering

These top AI prompt engineering courses offer a comprehensive guide into the evolving world of AI, catering to a wide range of learners from beginners to advanced practitioners. These courses not only equip individuals with the technical know-how of prompt engineering but also open doors to innovative applications in various fields. As AI continues to shape our digital landscape, mastering prompt engineering becomes crucial, empowering users to effectively communicate with and leverage AI technologies. Whether for career advancement, personal growth, or academic pursuits, these courses provide the foundational skills needed to navigate and excel in the dynamic realm of AI.



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Books, Courses & Certifications

Complete Guide with Curriculum & Fees

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The year 2025 for AI education provides choices catering to learning style, career goal, and budget. The Logicmojo Advanced Data Science & AI Program has emerged as the top one, offering comprehensive training with proven results in placement for those wishing to pursue job-oriented training. It offers the kind of live training, projects, and career support that fellow professionals seek when interested in turning into a high-paying AI position. 

On the other hand, for the independent learner seeking prestige credentials, a few other good options might include programs from Stanford, MIT, and DeepLearning.AI. Google and IBM certificates are an inexpensive footing for a beginner, while, at the opposite end of the spectrum, a Carnegie Mellon certificate is considered the ultimate academic credential in AI.

Whatever choice you make in 2025 to further your knowledge in AI will place you at the forefront of technology innovation. AI, expected to generate millions of jobs, has the potential to revolutionize every industry, and so whatever you learn today will be the deciding factor in your career waters for at least the next few decades. 



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Artificial Intelligence and Machine Learning Bootcamp Powered by Simplilearn

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Artificial Intelligence and Machine Learning are noteworthy game-changers in today’s digital world. Technological wonders once limited to science fiction have become science fact, giving us innovations such as self-driving cars, intelligent voice-operated virtual assistants, and computers that learn and grow.

The two fields are making inroads into all areas of our lives, including the workplace, showing up in occupations such as Data Scientist and Digital Marketer. And for all the impressive things that Artificial Intelligence and Machine Learning have accomplished in the last ten years, there’s so much more in store.

Become the Highest Paid AI Engineer!

With Our Trending AI Engineer Master ProgramKnow More

Simplilearn wants today’s IT professionals to be better equipped to embrace these new technologies. Hence, it offers Machine Learning Bootcamp, held in conjunction with Caltech’s Center for Technology and Management Education (CTME) and in collaboration with IBM.

The bootcamp covers the relevant points of Artificial Intelligence and Machine Learning, exploring tools and concepts such as Python and TensorFlow. The course optimizes the academic excellence of Caltech and the industry prowess of IBM, creating an unbeatable learning resource that supercharges your skillset and prepares you to navigate the world of AI/ML better.

Why is This a Great Bootcamp?

When you bring together an impressive lineup of Simplilearn, Caltech, and IBM, you expect nothing less than an excellent result. The AI and Machine Learning Bootcamp delivers as promised.

This six-month program deals with vital AI/ML concepts such as Deep Learning, Statistics, and Data Science With Python. Here is a breakdown of the diverse and valuable information the bootcamp offers:

  • Orientation. The orientation session prepares you for the rigors of an intense, six-month learning experience, where you dedicate from five to ten hours a week to learning the latest in AI/ML skills and concepts.
  • Introduction to Artificial Intelligence. There’s a difference between AI and ML, and here’s where you start to learn this. This offering is a beginner course covering the basics of AI and workflows, Deep Learning, Machine Learning, and other details.
  • Python for Data Science. Many data scientists prefer to use the Python programming language when working with AI/ML. This section deals with Python, its libraries, and using a Jupyter-based lab environment to write scripts.
  • Applied Data Science with Python. Your exposure to Python continues with this study of Python’s tools and techniques used for Data Analytics.
  • Machine Learning. Now we come to the other half of the AI/ML partnership. You will learn all about Machine Learning’s chief techniques and concepts, including heuristic aspects, supervised/unsupervised learning, and developing algorithms.
  • Deep Learning with Keras and Tensorflow. This section shows you how to use Keras and TensorFlow frameworks to master Deep Learning models and concepts and prepare Deep Learning algorithms.
  • Advanced Deep Learning and Computer Vision. This advanced course takes Deep Learning to a new level. This module covers topics like Computer Vision for OCR and Object Detection, and Computer Vision Basics with Python.
  • Capstone project. Finally, it’s time to take what you have learned and implement your new AI/ML skills to solve an industry-relevant issue.

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Become the Highest Paid AI Engineer!

The course also offers students a series of electives:

  • Statistics Essentials for Data Science. Statistics are a vital part of Data Science, and this elective teaches you how to make data-driven predictions via statistical inference.
  • NLP and Speech Recognition. This elective covers speech-to-text conversion, text-to-speech conversion, automated speech recognition, voice-assistance devices, and much more.
  • Reinforcement Learning. Learn how to solve reinforcement learning problems by applying different algorithms and strategies like TensorFlow and Python.
  • Caltech Artificial Intelligence and Machine Learning Bootcamp Masterclass. These masterclasses are conducted by qualified Caltech and IBM instructors.

This AI and ML Bootcamp gives students a bounty of AI/ML-related benefits like:

  • Campus immersion, which includes an exclusive visit to Caltech’s robotics lab.
  • A program completion certificate from Caltech CTME.
  • A Caltech CTME Circle membership.
  • The chance to earn up to 22 CEUs courtesy of Caltech CTME.
  • An online convocation by the Caltech CTME Program Director.
  • A physical certificate from Caltech CTME if you request one.
  • Access to hackathons and Ask Me Anything sessions from IBM.
  • More than 25 hands-on projects and integrated labs across industry verticals.
  • A Level Up session by Andrew McAfee, Principal Research Scientist at MIT.
  • Access to Simplilearn’s Career Service, which will help you get noticed by today’s top hiring companies.
  • Industry-certified certificates for IBM courses.
  • Industry masterclasses delivered by IBM.
  • Hackathons from IBM.
  • Ask Me Anything (AMA) sessions held with the IBM leadership.

And these are the skills the course covers, all essential tools for working with today’s AI and ML projects:

  • Statistics
  • Python
  • Supervised Learning
  • Unsupervised Learning
  • Recommendation Systems
  • NLP
  • Neural Networks
  • GANs
  • Deep Learning
  • Reinforcement Learning
  • Speech Recognition
  • Ensemble Learning
  • Computer Vision

About Caltech CTME

Located in California, Caltech is a world-famous, highly respected science and engineering institution featuring some of today’s brightest scientific and technological minds. Contributions from Caltech alumni have earned worldwide acclaim, including over three dozen Nobel prizes. Caltech CTME instructors offer this quality of learning to our students by holding bootcamp master classes.

About IBM

IBM was founded in 1911 and has earned a reputation as the top IT industry leader and master of IT innovation.

How to Thrive in the Brave New World of AI and ML

Machine Learning and Artificial Intelligence have enormous potential to change our world for the better, but the fields need people of skill and vision to help lead the way. Somehow, there must be a balance between technological advancement and how it impacts people (quality of life, carbon footprint, job losses due to automation, etc.).

The AI and Machine Learning Bootcamp helps teach and train students, equipping them to assume a role of leadership in the new world that AI and ML offer.



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Teaching Developers to Think with AI – O’Reilly

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Developers are doing incredible things with AI. Tools like Copilot, ChatGPT, and Claude have rapidly become indispensable for developers, offering unprecedented speed and efficiency in tasks like writing code, debugging tricky behavior, generating tests, and exploring unfamiliar libraries and frameworks. When it works, it’s effective, and it feels incredibly satisfying.

But if you’ve spent any real time coding with AI, you’ve probably hit a point where things stall. You keep refining your prompt and adjusting your approach, but the model keeps generating the same kind of answer, just phrased a little differently each time, and returning slight variations on the same incomplete solution. It feels close, but it’s not getting there. And worse, it’s not clear how to get back on track.

That moment is familiar to a lot of people trying to apply AI in real work. It’s what my recent talk at O’Reilly’s AI Codecon event was all about.

Over the last two years, while working on the latest edition of Head First C#, I’ve been developing a new kind of learning path, one that helps developers get better at both coding and using AI. I call it Sens-AI, and it came out of something I kept seeing:

There’s a learning gap with AI that’s creating real challenges for people who are still building their development skills.

My recent O’Reilly Radar article “Bridging the AI Learning Gap” looked at what happens when developers try to learn AI and coding at the same time. It’s not just a tooling problem—it’s a thinking problem. A lot of developers are figuring things out by trial and error, and it became clear to me that they needed a better way to move from improvising to actually solving problems.

From Vibe Coding to Problem Solving

Ask developers how they use AI, and many will describe a kind of improvisational prompting strategy: Give the model a task, see what it returns, and nudge it toward something better. It can be an effective approach because it’s fast, fluid, and almost effortless when it works.

That pattern is common enough to have a name: vibe coding. It’s a great starting point, and it works because it draws on real prompt engineering fundamentals—iterating, reacting to output, and refining based on feedback. But when something breaks, the code doesn’t behave as expected, or the AI keeps rehashing the same unhelpful answers, it’s not always clear what to try next. That’s when vibe coding starts to fall apart.

Senior developers tend to pick up AI more quickly than junior ones, but that’s not a hard-and-fast rule. I’ve seen brand-new developers pick it up quickly, and I’ve seen experienced ones get stuck. The difference is in what they do next. The people who succeed with AI tend to stop and rethink: They figure out what’s going wrong, step back to look at the problem, and reframe their prompt to give the model something better to work with.

When developers think critically, AI works better. (slide from my May 8, 2025, talk at O’Reilly AI Codecon)

The Sens-AI Framework

As I started working more closely with developers who were using AI tools to try to find ways to help them ramp up more easily, I paid attention to where they were getting stuck, and I started noticing that the pattern of an AI rehashing the same “almost there” suggestions kept coming up in training sessions and real projects. I saw it happen in my own work too. At first it felt like a weird quirk in the model’s behavior, but over time I realized it was a signal: The AI had used up the context I’d given it. The signal tells us that we need a better understanding of the problem, so we can give the model the information it’s missing. That realization was a turning point. Once I started paying attention to those breakdown moments, I began to see the same root cause across many developers’ experiences: not a flaw in the tools but a lack of framing, context, or understanding that the AI couldn’t supply on its own.

The Sens-AI framework steps (slide from my May 8, 2025, talk at O’Reilly AI Codecon)

Over time—and after a lot of testing, iteration, and feedback from developers—I distilled the core of the Sens-AI learning path into five specific habits. They came directly from watching where learners got stuck, what kinds of questions they asked, and what helped them move forward. These habits form a framework that’s the intellectual foundation behind how Head First C# teaches developers to work with AI:

  1. Context: Paying attention to what information you supply to the model, trying to figure out what else it needs to know, and supplying it clearly. This includes code, comments, structure, intent, and anything else that helps the model understand what you’re trying to do.
  2. Research: Actively using AI and external sources to deepen your own understanding of the problem. This means running examples, consulting documentation, and checking references to verify what’s really going on.
  3. Problem framing: Using the information you’ve gathered to define the problem more clearly so the model can respond more usefully. This involves digging deeper into the problem you’re trying to solve, recognizing what the AI still needs to know about it, and shaping your prompt to steer it in a more productive direction—and going back to do more research when you realize that it needs more context.
  4. Refining: Iterating your prompts deliberately. This isn’t about random tweaks; it’s about making targeted changes based on what the model got right and what it missed, and using those results to guide the next step.
  5. Critical thinking: Judging the quality of AI output rather than just simply accepting it. Does the suggestion make sense? Is it correct, relevant, plausible? This habit is especially important because it helps developers avoid the trap of trusting confident-sounding answers that don’t actually work.

These habits let developers get more out of AI while keeping control over the direction of their work.

From Stuck to Solved: Getting Better Results from AI

I’ve watched a lot of developers use tools like Copilot and ChatGPT—during training sessions, in hands-on exercises, and when they’ve asked me directly for help. What stood out to me was how often they assumed the AI had done a bad job. In reality, the prompt just didn’t include the information the model needed to solve the problem. No one had shown them how to supply the right context. That’s what the five Sens-AI habits are designed to address: not by handing developers a checklist but by helping them build a mental model for how to work with AI more effectively.

In my AI Codecon talk, I shared a story about my colleague Luis, a very experienced developer with over three decades of coding experience. He’s a seasoned engineer and an advanced AI user who builds content for training other developers, works with large language models directly, uses sophisticated prompting techniques, and has built AI-based analysis tools.

Luis was building a desktop wrapper for a React app using Tauri, a Rust-based toolkit. He pulled in both Copilot and ChatGPT, cross-checking output, exploring alternatives, and trying different approaches. But the code still wasn’t working.

Each AI suggestion seemed to fix part of the problem but break another part. The model kept offering slightly different versions of the same incomplete solution, never quite resolving the issue. For a while, he vibe-coded through it, adjusting the prompt and trying again to see if a small nudge would help, but the answers kept circling the same spot. Eventually, he realized the AI had run out of context and changed his approach. He stepped back, did some focused research to better understand what the AI was trying (and failing) to do, and applied the same habits I emphasize in the Sens-AI framework.

That shift changed the outcome. Once he understood the pattern the AI was trying to use, he could guide it. He reframed his prompt, added more context, and finally started getting suggestions that worked. The suggestions only started working once Luis gave the model the missing pieces it needed to make sense of the problem.

Applying the Sens-AI Framework: A Real-World Example

Before I developed the Sens-AI framework, I ran into a problem that later became a textbook case for it. I was curious whether COBOL, a decades-old language developed for mainframes that I had never used before but wanted to learn more about, could handle the basic mechanics of an interactive game. So I did some experimental vibe coding to build a simple terminal app that would let the user move an asterisk around the screen using the W/A/S/D keys. It was a weird little side project—I just wanted to see if I could make COBOL do something it was never really meant for, and learn something about it along the way.

The initial AI-generated code compiled and ran just fine, and at first I made some progress. I was able to get it to clear the screen, draw the asterisk in the right place, handle raw keyboard input that didn’t require the user to press Enter, and get past some initial bugs that caused a lot of flickering.

But once I hit a more subtle bug—where ANSI escape codes like ";10H" were printing literally instead of controlling the cursor—ChatGPT got stuck. I’d describe the problem, and it would generate a slightly different version of the same answer each time. One suggestion used different variable names. Another changed the order of operations. A few attempted to reformat the STRING statement. But none of them addressed the root cause.

The COBOL app with a bug, printing a raw escape sequence instead of moving the asterisk.

The pattern was always the same: slight code rewrites that looked plausible but didn’t actually change the behavior. That’s what a rehash loop looks like. The AI wasn’t giving me worse answers—it was just circling, stuck on the same conceptual idea. So I did what many developers do: I assumed the AI just couldn’t answer my question and moved on to another problem.

At the time, I didn’t recognize the rehash loop for what it was. I assumed ChatGPT just didn’t know the answer and gave up. But revisiting the project after developing the Sens-AI framework, I saw the whole exchange in a new light. The rehash loop was a signal that the AI needed more context. It got stuck because I hadn’t told it what it needed to know.

When I started working on the framework, I remembered this old failure and thought it’d be a perfect test case. Now I had a set of steps that I could follow:

  • First, I recognized that the AI had run out of context. The model wasn’t failing randomly—it was repeating itself because it didn’t understand what I was asking it to do.
  • Next, I did some targeted research. I brushed up on ANSI escape codes and started reading the AI’s earlier explanations more carefully. That’s when I noticed a detail I’d skimmed past the first time while vibe coding: When I went back through the AI explanation of the code that it generated, I saw that the PIC ZZ COBOL syntax defines a numeric-edited field. I suspected that could potentially cause it to introduce leading spaces into strings and wondered if that could break an escape sequence.
  • Then I reframed the problem. I opened a new chat and explained what I was trying to build, what I was seeing, and what I suspected. I told the AI I’d noticed it was circling the same solution and treated that as a signal that we were missing something fundamental. I also told it that I’d done some research and had three leads I suspected were related: how COBOL displays multiple items in sequence, how terminal escape codes need to be formatted, and how spacing in numeric fields might be corrupting the output. The prompt didn’t provide answers; it just gave some potential research areas for the AI to investigate. That gave it what it needed to find the additional context it needed to break out of the rehash loop.
  • Once the model was unstuck, I refined my prompt. I asked follow-up questions to clarify exactly what the output should look like and how to construct the strings more reliably. I wasn’t just looking for a fix—I was guiding the model toward a better approach.
  • And most of all, I used critical thinking. I read the answers closely, compared them to what I already knew, and decided what to try based on what actually made sense. The explanation checked out. I implemented the fix, and the program worked.
My prompt that broke ChatGPT out of its rehash loop

Once I took the time to understand the problem—and did just enough research to give the AI a few hints about what context it was missing—I was able to write a prompt that broke ChatGPT out of the rehash loop, and it generated code that did exactly what I needed. The generated code for the working COBOL app is available in this GitHub GIST.

The working COBOL app that moves an asterisk around the screen

Why These Habits Matter for New Developers

I built the Sens-AI learning path in Head First C# around the five habits in the framework. These habits aren’t checklists, scripts, or hard-and-fast rules. They’re ways of thinking that help people use AI more productively—and they don’t require years of experience. I’ve seen new developers pick them up quickly, sometimes faster than seasoned developers who didn’t realize they were stuck in shallow prompting loops.

The key insight into these habits came to me when I was updating the coding exercises in the most recent edition of Head First C#. I test the exercises using AI by pasting the instructions and starter code into tools like ChatGPT and Copilot. If they produce the correct solution, that means I’ve given the model enough information to solve it—which means I’ve given readers enough information too. But if it fails to solve the problem, something’s missing from the exercise instructions.

The process of using AI to test the exercises in the book reminded me of a problem I ran into in the first edition, back in 2007. One exercise kept tripping people up, and after reading a lot of feedback, I realized the problem: I hadn’t given readers all the information they needed to solve it. That helped connect the dots for me. The AI struggles with some coding problems for the same reason the learners were struggling with that exercise—because the context wasn’t there. Writing a good coding exercise and writing a good prompt both depend on understanding what the other side needs to make sense of the problem.

That experience helped me realize that to make developers successful with AI, we need to do more than just teach the basics of prompt engineering. We need to explicitly instill these thinking habits and give developers a way to build them alongside their core coding skills. If we want developers to succeed, we can’t just tell them to “prompt better.” We need to show them how to think with AI.

Where We Go from Here

If AI really is changing how we write software—and I believe it is—then we need to change how we teach it. We’ve made it easy to give people access to the tools. The harder part is helping them develop the habits and judgment to use them well, especially when things go wrong. That’s not just an education problem; it’s also a design problem, a documentation problem, and a tooling problem. Sens-AI is one answer, but it’s just the beginning. We still need clearer examples and better ways to guide, debug, and refine the model’s output. If we teach developers how to think with AI, we can help them become not just code generators but thoughtful engineers who understand what their code is doing and why it matters.



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