Books, Courses & Certifications
22 Top AI Certifications to Know
Demand for professionals with artificial intelligence skills has soared, with a McKinsey survey finding that over 70 percent of companies have adopted AI and a PwC survey confirming that jobs requiring AI specialist skills have grown over three times faster than all other jobs.
Top AI Certification Providers
- IBM
- Microsoft
- United States Artificial Intelligence Institute
- Nvidia
- CertNexus
- Stanford University
- Artificial Intelligence Board of America
To help, many organizations are rolling out AI certification programs, courses and other training opportunities as a way for people to bolster their AI abilities.
Why Get an AI Certification?
Up to 75 percent of knowledge workers across the world are already using generative AI tools, according to a Microsoft report. Responses in the same report further reveal that 71 percent of leaders prefer candidates with AI skills over more experienced candidates, and 77 percent would give early-career candidates with AI skills more responsibilities.
As a result, achieving an AI certification not only leads to greater job security for employees but also opens the door to career advancement opportunities. AI expertise provides a major advantage for job candidates as well, serving as a way to stand out from a packed talent pool and accelerate one’s career development.
22 Top AI Certifications to Know
Whether you’re a seasoned professional looking to upskill, or a novice taking your first steps into the world of AI, these certifications can help increase your credibility in this highly competitive job market.
1. Professional Certificate in Computer Science for Artificial Intelligence – Harvard University
- Who it’s for: Any student or professional who wants to gain a broader understanding of computer science, programming and AI.
- Topics and skills covered: Computer science, programming, AI algorithms, AI principles, machine learning and how to use AI in Python.
- Cost: $466.20, five months with 7-22 hours of learning per week.
- Experience needed: No prior experience in computer science or AI is required.
- Additional features: Certificate series includes two CS50 courses taught by Harvard computer science instructors.
2. Artificial Intelligence Engineer Certification – Artificial Intelligence Board of America
- Who it’s for: Professionals who want to build a career in AI engineering.
- Topics and skills covered: Machine learning models, deep learning, natural language processing, human-computer interaction, cognitive computing and solving data and business challenges with AI.
- Cost: $550, 180 days, self-paced.
- Experience needed: Students must have at least an associate’s degree, as well as basic programming skills.
- Additional features: Participants receive books and access to online materials before taking a certification exam within 180 days of registration.
3. Professional Certificate Program in Machine Learning & Artificial Intelligence – MIT Professional Education
- Who it’s for: Professionals who work in a technical field and want to learn how to apply AI and machine learning to data analysis.
- Topics and skills covered: Machine learning, data analytics, deep learning and applying AI and machine learning to data analytics and mathematical concepts.
- Cost: Price depends on courses selected, 36 months, pace depends on courses and instructors.
- Experience needed: At least a bachelor’s degree and three years of experience working in a technical field is required.
- Additional features: Participants can supplement two core courses with a range of electives that cover AI ethics, computer vision and other topics.
4. AI Engineering Professional Certificate – IBM
- Who it’s for: Professionals who want to build a career as an AI or ML engineer.
- Topics and skills covered: Fundamentals of machine learning and deep learning, programming languages like Python, computer vision, NLP, object recognition and applying ML to big data.
- Cost: Free with Coursera account, two months with 10 hours of learning per week, self-paced.
- Experience needed: Intermediate coding experience and a background working as an AI or ML engineer.
- Additional features: Participants who complete the program earn a LinkedIn certificate and gain access to resume, interview and career support.
5. NVIDIA-Certified Associate Generative AI LLMs – NVIDIA
- Who it’s for: Professionals in technical roles like data scientist, ML engineer and software engineer who are looking to gain foundational knowledge of generative AI, large language models (LLMs) and how to apply basic concepts to AI applications.
- Topics and skills covered: Machine learning, neural networks, prompt engineering, data analysis, software development, Python libraries and deploying LLMs.
- Cost: $135, one-hour time limit.
- Experience needed: Basic knowledge of generative AI and LLMs is required.
- Additional features: Participants who pass the exam receive a certification that lasts for two years before the exam must be retaken.
6. Fundamentals of Google AI for Web-Based Machine Learning – Google
- Who it’s for: Professionals looking to learn the fundamentals of AI, ML and deep learning — and how they relate to each other and data.
- Topics and skills covered: AI basics, machine learning, deep learning, programming languages like JavaScript (JS), ML libraries and building web applications.
- Cost: $538.20, three months with several hours of learning per week, self-paced.
- Experience needed: No prior experience is required.
- Additional features: The program consists of two courses — a Google AI course for beginners and a Google AI course for JS developers.
7. Generative AI with Large Language Models – AWS and DeepLearning.ai
- Who it’s for: Professionals with a foundational understanding of AI topics looking to integrate generative AI and LLMs into their work.
- Topics and skills covered: Generative AI principles, building LLMs, training models and applying generative AI to business scenarios.
- Cost: Free with Coursera account, three weeks with five hours of learning per week, self-paced.
- Experience needed: Prior experience in Python, machine learning basics, working with training data and taking the ML or deep learning specialization from DeepLearning.AI is required.
- Additional features: The course consists of three separate modules and culminates with participants receiving a LinkedIn certificate upon completion.
8. Artificial Intelligence A-Z 2024: Build 7 AI + LLM and ChatGPT – SuperDataScience Team
- Who it’s for: Students and professionals looking to develop their knowledge of AI, machine learning and deep learning.
- Topics and skills covered: AI, machine learning, deep learning, reinforcement learning, building AI applications, LLMs, deep q-learning and convolutional neural networks.
- Cost: $199.99, 15.5 hours, self-paced.
- Experience needed: Basic knowledge of Python and completion of high-school-level math is required.
- Additional features: Participants who complete the course get access to three additional AI models that can be used for a humanoid and a self-driving car.
9. Artificial Intelligence Graduate Certificate – Stanford University
- Who it’s for: Professionals interested in developing more in-depth AI expertise and how to apply AI in their workplaces.
- Topics and skills covered: AI principles, machine learning, deep learning, probability models, computer vision, robotics, NLP, data mining and AI-driven decision-making.
- Cost: $19,682-$24,224, up to two years with 15-20 hours per week of learning.
- Experience needed: A bachelor’s degree with a GPA of at least 3.0, college-level calculus and algebra, programming experience and knowledge of probability theory and probability distribution concepts are required.
- Additional features: Participants must complete two core courses and three electives, receiving a certificate for every course where they earn a B grade or higher.
10. Microsoft Certified: Azure AI Engineer Associate – Microsoft
- Who it’s for: Professionals who work as AI engineers and want to demonstrate their expertise in the Microsoft Azure AI platform.
- Topics and skills covered: Azure AI basics, programming languages like C#, responsible AI principles, NLP solutions, computer vision solutions and building AI applications.
- Cost: $165, 100-minute time limit.
- Experience needed: Experience working as an AI engineer and working with Microsoft Azure AI is required.
- Additional features: Participants gain access to exam preparation resources, including a practice assessment and training videos.
11. AI for Everyone – DeepLearning.ai
- Who it’s for: Anyone interested in developing a foundational knowledge of AI.
- Topics and skills covered: AI common terms, AI’s capabilities, AI business applications, AI strategy and AI ethics.
- Cost: Free with a Coursera account, three weeks with two hours of learning per week, self-paced.
- Experience needed: No prior experience required.
- Additional features: The course is made up of four modules, and participants who complete the course earn a LinkedIn certificate.
12. Artificial Intelligence: Business Strategies and Applications – University of California – Berkeley
- Who it’s for: Professionals looking to learn the basics of AI and how to leverage AI technologies in business settings.
- Topics and skills covered: AI’s capabilities, generative AI applications, running AI applications, machine learning basics, deep learning, robotics and common AI pitfalls.
- Cost: $2,714, two months with four to six hours of learning per week.
- Experience needed: No prior technical experience is required.
- Additional features: Participants are taught by instructors through live online sessions and complete a capstone project at the end of the course.
13. Designing and Building AI Products and Services – MIT xPro
- Who it’s for: UX and UI designers, AI startup founders, technology consultants, technical product managers and those in other technology professions.
- Topics and skills covered: AI product design, building AI models, machine learning algorithms, human-machine interfaces and problem-solving with ML techniques.
- Cost: $2,832, eight weeks with six hours of learning per week.
- Experience needed: Experience in a technical profession and basic knowledge of calculus, linear algebra, statistics, probabilities and Python is required.
- Additional features: Participants get to present an AI project proposal to internal stakeholders and investors, and they earn five continuing education units upon completing the course.
14. AI Developer Professional Certificate – IBM
- Who it’s for: Professionals looking to acquire the skills needed to launch a career in an AI profession.
- Topics and skills covered: Software engineering AI fundamentals, generative AI, prompt engineering, programming languages like HTML and building AI applications.
- Cost: Free with Coursera account, six months with four hours of learning per week, self-paced.
- Experience needed: No AI or programming experience is required.
- Additional features: Participants who complete the course are awarded with an IBM digital badge and display employable skills to launch an AI career in six months.
15. Jetson AI Certification – NVIDIA
- Who it’s for: The AI Specialist course is open to anyone looking to broaden their AI knowledge while the AI Ambassador course is tailored to educators who want to enhance their AI expertise.
- Topics and skills covered: NVIDIA Jetson basics, Jupyter Notebook, training deep neural networks and machine learning.
- Cost: Free, self-paced.
- Experience needed: Basic knowledge of Python and Linux for both courses while teaching experience is required for the Ambassador course.
- Additional features: Participants must complete a hands-on assessment where they submit an open-source project that involves problem-solving using NVIDIA Jetson.
16. Professional Machine Learning Engineer Certification – Google Cloud
- Who it’s for: Professionals who work as ML engineers or other relevant technical roles who want to demonstrate their expertise in ML engineering.
- Topics and skills covered: Building ML models, developing AI apps, training models, data processing, running ML experiments, automating model training, tracking metadata and testing ML solutions.
- Cost: $200, two-hour time limit.
- Experience needed: At least three years of relevant work experience with one year of developing and managing Google Cloud solutions is required.
- Additional features: Participants can review an exam guide, practice with sample questions and join webinars to prepare for the certification exam.
17. Post Graduate Program in AI and Machine Learning – Purdue University
- Who it’s for: Professionals looking to advance their careers in AI- and ML-related fields.
- Topics and skills covered: AI, ML, deep learning, Python, data science, generative AI basics, prompt engineering, ChatGPT, NLP, speech recognition, computer vision and reinforcement learning.
- Cost: $4,300, 11 months, pace depends on instructors.
- Experience needed: A bachelor’s degree with at least 50-percent marks, basic knowledge of programming and mathematics concepts and at least two years of relevant work experience are required.
- Additional features: Participants can engage with over 25 hands-on projects and practice using over 20 AI tools.
18. The Graduate Certificate in Ethical Artificial Intelligence – San Francisco State University
- Who it’s for: Graduate students and professionals interested in strengthening their understanding of AI ethics and the social and legal implications of using AI.
- Topics and skills covered: AI ethics, data mining, pattern analysis, AI compliance, AI applications and philosophical issues with AI.
- Cost: Depends on registration status with SFSU, 10 course units, pace depends on instructors.
- Experience needed: SFSU students must be graduate students and follow university procedures while non-SFSU students must have at least a bachelor’s degree with a 3.0 GPA and apply through Cal State Apply.
- Additional features: Participants must complete a series of courses, culminating in a research and reflection paper that includes three courses of independent study.
19. CertNexus Certified Artificial Intelligence Practitioner Professional Certification – CertNexus
- Who it’s for: Data science professionals transitioning to the AI field who want to stand out from other job candidates and professionals.
- Topics and skills covered: Business problem-solving with AI and ML, ML workflows, designing ML models and building decision trees and neural networks.
- Cost: Free with Coursera account, two months with 10 hours of learning per week, self-paced.
- Experience needed: Basic knowledge of AI concepts, experience with databases and experience with advanced programming languages are required.
- Additional features: Participants who complete the course earn a LinkedIn certificate and can achieve industry certification after passing the exam.
20. Applied Generative AI for Digital Transformation – MIT Professional Education
- Who it’s for: Professionals of various backgrounds, especially senior leaders, technology leaders, senior managers, mid-career executives, innovation managers, sales and product managers, marketing professionals, customer experience professionals and venture capital investors.
- Topics and skills covered: Generative AI, automation strategies, digital transformation, reinforcement learning, prompt engineering, AI ethics and the risks of AI.
- Cost: $3,125, three weeks with up to 14 hours of learning per week.
- Experience needed: No prior experience is required.
- Additional features: Participants who finish the course know how to use generative AI tools and automate workflows to improve business processes.
21. Certified Artificial Intelligence Scientist – United States Artificial Intelligence Institute
- Who it’s for: Senior-level professionals and leaders in the AI and business fields who want to sharpen their AI expertise and learn how to strategically apply AI to business contexts.
- Topics and skills covered: AI essentials, machine learning techniques, deep learning fundamentals, computer vision, generative AI, product management, explainable AI, AI ethics, engineering management and deploying AI and ML in the cloud.
- Cost: $894, 4-25 weeks with 8-10 hours of learning per week, self-paced.
- Experience needed: A bachelor’s degree in the STEM field and at least five years of work experience in AI or a related field are required for the initial path, with stricter requirements for more advanced paths.
- Additional features: The USAII provides an online resource center that includes study books and self-paced videos to help participants reinforce and practice new concepts.
22. ChatGPT / AI Ethics: Ethical Intelligence for 2024
- Who it’s for: Leaders and managers who want to ensure they apply AI ethically within their organizations.
- Topics and skills covered: AI ethical principles, practicing confidentiality in the digital age, fairness, bias, intellectual property and the ethical power of caring.
- Cost: $74.99, three hours, self-paced.
- Experience needed: No prior experience is required.
- Additional features: Participants close out the course by considering real-world examples of AI ethics including AI in college essays, voice cloning and AI used in filmmaking.
How to Choose an AI Certification
An AI certification course or program is only effective if it meets the needs of its participants. When it comes to selecting the right option for you, here are a few factors to consider.
1. Establish Your Career Goals
Define your career goals before looking at courses. For example, are you interested in an AI certification program because you want to learn new skills for your current role, become qualified for a higher-ranking role or make a career change? Answering this question can help you narrow down offerings to the ones most relevant to your career ambitions.
2. Determine Personal Capacity
Keep your personal boundaries in mind when exploring AI certifications. Consider variables like your financial limits, availability and learning preferences. Making sure a course aligns with your personal circumstances will ensure you have the best learning experience possible.
3. Study the Course Content
Review the curriculum to see if it addresses topics you’re interested in. Does the course content include skills, technologies and techniques relevant to your career goals or industry? Check whether a program supplements lessons with hands-on projects, which can translate into real-world experience that employers are looking for.
4. Assess the Return on Investment
Reflect on the benefits of the course and how it helps you get to where you want to go. Does it come with a certification recognized by industry leaders? Does it offer networking opportunities with top companies and professionals? Think beyond the course content and weigh any lasting advantages to decide whether it’s a worthy investment.
How much does it cost to get AI certified?
The cost of getting AI certified varies widely, since prices differ across formats and institutions. While some programs take months to complete and cost thousands of dollars, others are free, one-off courses on sites like Coursera and Udemy that are sponsored by accredited universities and tech companies.
What can you do with an AI certificate?
Once you earn an AI certification, you can use it to pursue a career in the AI industry. An AI certification serves as validation of your knowledge and skills in artificial intelligence, and may give you a competitive edge among other candidates in the job market.
Are AI certifications worth it?
Those who earn AI certifications can enjoy lasting benefits throughout their careers, including being high-demand job candidates, receiving more opportunities for career advancement and enjoying as much as a 25-percent wage increase in some industries.
Can you get an AI certification without computer science experience?
Yes, professionals without computer science experience are able to achieve an AI certification. Many courses and programs don’t require coding abilities and are accessible to professionals with varying technical skills.
Certification vs. certificate
A certificate is awarded to a participant who has completed a course or series of courses, usually as part of an academic program. On the other hand, a certification serves as proof that a participant has not only completed a professional training program, but also has the qualifications and knowledge required for a specific role or area of expertise. A professional may also need to complete ongoing training to maintain a certification.
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.
Books, Courses & Certifications
Artificial Intelligence and Machine Learning Bootcamp Powered by Simplilearn
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.
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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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.
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.
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.
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:
- 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.
- 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.
- 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.
- 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.
- 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 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 ZZCOBOL 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.
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.
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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