Books, Courses & Certifications
How to Become an AI Prompt Engineer [2025]
In artificial intelligence (AI), where models and systems are becoming increasingly sophisticated, a new field has emerged that plays a crucial role in the interaction between humans and AI: Prompt Engineering. While still in its infancy, this field is rapidly gaining recognition for its importance in optimizing AI outputs. In this article, we’ll delve into what prompt engineering is and the responsibilities of a prompt engineer and guide you on how to become a prompt engineer, a career path that is becoming increasingly relevant in our technology-driven world.
What Is Prompt Engineering?
Prompt engineering is designing and formulating prompts or inputs given to AI models to generate desired outputs or responses. This discipline is particularly relevant in large language models (LLMs) and generative AI, where the quality and specificity of the input significantly influence the relevance, accuracy, and creativity of the AI’s output. Prompt engineering is not about asking the right questions; it’s an art and science that combines the understanding of the AI model’s capabilities, creativity in input design, and strategic thinking to achieve specific objectives.
What Does a Prompt Engineer Do?
A prompt engineer operates at the intersection of technology, psychology, and linguistics, playing a multifaceted role that includes:
- Designing Effective Prompts: Crafting questions or commands that lead AI models to produce accurate, relevant, high-quality responses. This involves understanding the nuances of language, the model’s training data, and capabilities.
- Optimization: Continuously testing and refining prompts to improve AI responses. This iterative process is crucial for enhancing the performance of AI applications in real-world scenarios.
- Customization: Tailoring prompts to meet specific needs of various applications, such as content creation, programming, customer service, and more, ensuring that AI outputs align with user expectations and requirements.
- Training and Development: Contributing to the training of AI models by providing feedback on outputs and suggesting adjustments to improve understanding and response generation.
- Cross-functional Collaboration: Working closely with developers, data scientists, and subject matter experts to integrate AI capabilities into products and services effectively.
Fun Fact: Who knew writing the right questions for AI could be one of the most lucrative and in-demand careers of the AI era! 💡💰
How to Become a Prompt Engineer?
Becoming a prompt engineer involves formal education, skill development, and hands-on experience in artificial intelligence (AI) and natural language processing (NLP). Here’s a structured approach to how to embark on this innovative career path:
1. Acquire a Strong Educational Background
- Pursue Relevant Education: A bachelor’s degree in computer science, linguistics, cognitive science, data science, or a related field provides a solid foundation. Courses in AI, machine learning (ML), and natural language processing (NLP) are particularly relevant.
- Understand AI and Machine Learning: Gain a deep understanding of how AI and ML models work, with a focus on language models. Knowledge of algorithms, statistics, and data structures is crucial.
2. Develop Technical Skills
- Learn Programming Languages: Proficiency in Python is essential since it’s widely used in AI development. Familiarity with AI frameworks like TensorFlow or PyTorch is also beneficial.
- NLP and Text Processing: Learn the basics of NLP, including text preprocessing, sentiment analysis, and language generation. Understanding these concepts is key to crafting effective prompts.
- Experiment with AI Models: For practical experience, use platforms like OpenAI’s GPT (Generative Pre-trained Transformer). Experimenting with different prompts and observing the outcomes is a great learning method.
3. Enhance Creative and Analytical Abilities
- Problem-Solving Skills: Develop your ability to think critically and creatively to craft prompts that yield meaningful and accurate responses from AI models.
- Analytical Skills: Learn to analyze the responses generated by AI to understand how changes in prompts affect the outputs.
4. Gain Hands-On Experience
- Projects and Internships: Participate in projects that require interaction with AI models. Internships in AI or data science can provide valuable experience and exposure.
- Contribute to Open-Source Projects: Engage with the AI community by contributing to open-source projects. It’s an excellent way to gain experience and improve your skills.
5. Stay Updated and Network
- Continuous Learning: The field of AI evolves rapidly. Stay updated on the latest research, tools, and best practices in AI and prompt engineering.
- Networking: Join AI and tech communities and attend workshops, conferences, and webinars. Networking with professionals in the field can provide insights into emerging trends and career opportunities.
6. Build a Portfolio
- Document Your Work: Create a portfolio showcasing your prompt engineering projects. Include case studies that demonstrate how your prompts improved AI model outputs.
7. Start Applying
- Look for Job Opportunities: Look for roles that specifically mention prompt engineering or require AI interaction, NLP, and creative problem-solving skills.
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Importance of Prompt Engineering
The rise of artificial intelligence technologies, especially in natural language processing and generative AI, has brought to light the importance of prompt engineering. Despite being a relatively new field, it is quickly proving to be essential for various reasons. Here are some key points underscoring the significance of prompt engineering and insights on how to become a prompt engineer:
Enhancing AI Model Performance
- Precision in Outputs: Prompt engineering is essential for eliciting precise and relevant responses from AI models. The quality of input directly affects the quality of output, making the skill of crafting effective prompts invaluable.
- Reducing Ambiguity: Well-designed prompts reduce ambiguity in AI responses, leading to more accurate and valuable outputs. This is particularly crucial in applications where precision and reliability are paramount, such as medical diagnostics or legal advice.
Expanding the Usability of AI Systems
- Customization: Through prompt engineering, AI systems can be tailored to meet specific needs across different domains, from creative writing and design to technical coding assistance and customer service. This customization enhances the utility and flexibility of AI technologies.
- Accessibility: Effective prompts make AI technologies more accessible to users without deep technical expertise, democratizing the benefits of AI.
Facilitating Better Human-AI Interaction
- User Experience: Prompt engineering significantly improves the user experience by ensuring that interactions with AI systems are more intuitive, engaging, and productive.
- Communication Efficiency: It streamlines the communication process between humans and AI, making it more efficient and reducing the need for multiple iterations to reach the desired outcome.
Advancing AI Development
- Feedback Loop for AI Training: Prompt engineering provides a vital feedback loop for AI development. By analyzing how different prompts affect AI responses, developers can gain insights into model behavior, identifying areas for improvement and refinement.
- Innovation: The field encourages innovation in AI interaction patterns, developing new methodologies and approaches to push the boundaries of what AI systems can achieve.
Economic and Societal Impacts
- Cost Reduction: Prompt engineering can lead to significant cost savings for businesses and organizations by improving the efficiency and accuracy of AI systems. It optimizes the time and resources spent on tasks AI can assist with, such as data analysis, content creation, and customer support.
- Empowerment and Ethical Use: Effective prompt engineering can empower users to leverage AI in ethical ways, guiding AI systems to generate content that is unbiased, fair, and aligned with societal norms and values.
Skills Required for Prompt Engineer
A diverse set of skills encompassing technical expertise, creativity, and critical thinking is essential to excel as a prompt engineer and navigate the path of how to become a prompt engineer. Here are the key skills required for someone looking to enter and succeed in the field of prompt engineering:
Technical and Analytical Skills
- Programming Proficiency: Knowledge of programming languages, especially Python, is crucial due to its widespread use in AI and machine learning. This skill is essential for scripting, automation, and interacting with AI models.
- Understanding of AI and Machine Learning: It is vital to have a deep understanding of artificial intelligence, machine learning principles, and how large language models (LLMs) work. This includes familiarity with model architectures, training processes, and the underlying technology of generative AI.
- Natural Language Processing (NLP): Since prompt engineering heavily involves working with language models, a strong grasp of NLP concepts and techniques is necessary. This includes text preprocessing, sentiment analysis, language generation, and understanding of linguistic nuances.
- Data Analysis: It is important to analyze and interpret data and understand the output of AI models. This helps refine prompts and improve model responses.
Relevant Read: Prompt Engineering Tools to Elevate AI Efficiency in 2025 📖
Creativity and Linguistic Skills
- Creative Thinking: Coming up with innovative and effective prompts requires creativity. A prompt engineer must think outside the box to design prompts that guide AI models to produce desired outcomes.
- Linguistic Sensitivity: Understanding language, syntax, semantics, and pragmatics helps craft prompts that are clear, precise, and likely to elicit accurate responses from AI models.
- Problem-Solving: The ability to troubleshoot and refine prompts based on the quality of the AI’s responses is a critical skill. It involves iterative testing and modification to achieve the best results.
Soft Skills
- Communication: Clear and effective communication is necessary, especially when working in teams or explaining complex concepts to non-technical stakeholders.
- Adaptability: AI and machine learning fields evolve rapidly. Being adaptable and open to continuous learning is crucial to stay updated with the latest developments and technologies.
- Collaboration: Prompt engineers often work with cross-functional teams, including data scientists, AI researchers, product managers, and UX designers. Thus, teamwork and the ability to collaborate effectively are important.
Average Prompt Engineer Salary
Experience
United States
Europe
Asia
Entry-Level
$70,000 – $100,000
€40,000 – €60,000
$20,000 – $50,000
Mid-Level
$100,000 – $150,000
€60,000 – €90,000
$50,000 – $80,000
Senior-Level
$150,000+
€90,000+
$80,000+
Additional Considerations
- Company Size and Type: Larger tech companies and startups in the growth phase may offer higher salaries, stock options, or other incentives than established non-tech companies.
- Specialization and Skills: Higher salaries can also be commanded by those with specialized skills in emerging AI technologies, a strong portfolio of projects, or unique expertise in prompt engineering.
- Educational Background: While not always the case, a higher educational level (e.g., Masters or Ph.D. in relevant fields) may lead to better salary offers.
Did You Know? 🔍
The Generative AI market is projected to grow at an annual rate of 41.53% (CAGR), reaching over $355 billion by 2030!
Career Opportunity for Prompt Engineer
As businesses and organizations increasingly incorporate AI into their operations, the demand for skilled prompt engineers is growing swiftly, highlighting a promising pathway for those wondering how to become a prompt engineer. The field demands a unique combination of expertise, understanding of AI technologies, creativity and a strong grasp of language. This distinct skill set equips prompt engineers for diverse roles across various industries, paving the way for diverse career opportunities for those interested in becoming a prompt engineer. Here are some key areas where prompt engineers can find rewarding career opportunities:
Technology and Software Companies
- AI and Machine Learning Startups: With AI startups flourishing, there’s a growing demand for professionals who can optimize interactions with AI models. Prompt engineers can be crucial in product development, ensuring AI systems generate relevant and accurate outputs.
- Big Tech Companies: Major tech companies investing in AI research and development offer roles for prompt engineers to refine AI models’ ability to understand and generate human-like text, enhancing products like virtual assistants, chatbots, and content generation tools.
Content Creation and Media
- Digital Marketing and Advertising: Companies can use AI to generate creative content for marketing campaigns. Prompt engineers can ensure that AI-generated content aligns with brand voice and campaign goals.
- Entertainment and Gaming: In the gaming industry and other entertainment sectors, prompt engineers can contribute to creating dynamic, AI-driven narratives and dialogues.
Education and Research
- Educational Technology: EdTech companies are leveraging AI to provide personalized learning experiences. Prompt engineers can help design AI systems that offer tailored educational content and tutoring.
- Academic Research and Development: Universities and research institutions may employ prompt engineers to work on cutting-edge AI research, contributing to advancements in natural language processing and generative AI models.
Customer Service and Support
- E-Commerce and Retail: Enhancing customer support with AI-driven chatbots that can handle inquiries and provide recommendations. Prompt engineers improve the efficacy and personalization of customer interactions.
- Financial Services: In banking and finance, prompt engineers can enhance AI applications for customer service, fraud detection, and personalized financial advice.
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Healthcare
- Medical Information and Support: AI applications in healthcare, such as patient support chatbots and informational tools, can be optimized by prompt engineers to provide accurate and relevant medical advice and support.
Legal and Compliance
- Automated Legal Assistance: Law firms and legal departments use AI to draft documents and offer legal advice. Prompt engineers can refine these systems to understand better and generate legal language and content.
Freelancing and Consultancy
- Independent Consulting: Experienced prompt engineers have opportunities to work as consultants for businesses seeking to implement or improve their use of AI, offering expertise on how to learn prompt engineering and how to best interact with and utilize AI models.
- Online Content Platforms: Platforms requiring content generation or moderation can benefit from prompt engineers to guide AI in producing or filtering content.
Future of Prompt Engineering
The future of prompt engineering looks promising and pivotal, with its trajectory closely intertwined with the advancements and proliferation of artificial intelligence (AI) technologies. As AI systems, especially generative models like GPT (Generative Pretrained Transformer), become more sophisticated and integrated into various aspects of daily life and industry, the role of prompt engineering is set to evolve in several key ways:
1. Increasing Importance in AI Interaction
Prompt engineering will become increasingly important as the primary means of interacting with AI systems. As AI models grow in complexity and capability, effectively communicating with these systems through well-crafted prompts becomes crucial. This interaction not only dictates the utility of AI in current applications but also opens up new possibilities for how AI can be applied across different sectors.
2. Expansion into Various Industries
While initially most prominent in the tech and data science sectors, prompt engineering is expanding into various fields, including healthcare, education, entertainment, and more. This growth is driven by the increasing adoption of AI solutions tailored to specific industry needs, where prompt engineering plays a critical role in customizing and optimizing these AI interactions.
3. Development of Specialized Tools and Platforms
As the demand for efficient and effective AI interactions grows, we can expect the development of specialized tools and platforms designed to assist in prompt engineering. These tools might offer functionalities like prompt optimization, response analysis, and even automated prompt generation, making it easier for non-experts to leverage the power of AI in their work.
Generative AI experts are shaping the future and this is your chance to become one of them! 🎯
4. Enhanced Focus on Ethical and Responsible AI Use
The future of prompt engineering will also involve a stronger focus on AI’s ethical and responsible use. Prompt engineers will be at the forefront of designing prompts that avoid bias, respect privacy, and ensure that AI responses align with ethical standards. This will maintain public trust in AI technologies and their applications.
5. Career Opportunities and Specializations
As the field matures, we’ll likely see a formalization of the career path for prompt engineers, with specialized training programs, certifications, and roles emerging. This specialization could lead to roles focused on specific aspects of prompt engineering, such as ethical prompt design, AI personality crafting, and industry-specific AI interaction optimization.
6. Integration with Emerging Technologies
Prompt engineering will not only evolve within the realm of AI but also in conjunction with other emerging technologies like virtual reality (VR), augmented reality (AR), and the Internet of Things (IoT). Integrating AI with these technologies, facilitated by effective, prompt engineering, could lead to more immersive, interactive, and personalized user experiences.
7. Contribution to AI Research and Development
Prompt engineering will continue to contribute valuable insights to AI research and development. By understanding how different prompts affect AI behavior and outputs, prompt engineers can provide feedback that helps improve AI models’ understanding and generation capabilities.
Conclusion
Becoming a prompt engineer opens up a world of possibilities in the rapidly evolving landscape of artificial intelligence. As we’ve explored, AI mastery combines technical knowledge, creative thinking, and continuous learning. The Generative AI for Business Transformation course offers an exceptional opportunity. This course, designed to bridge the gap between AI potential and practical business applications, provides the foundational knowledge and skills needed to excel in prompt engineering and beyond.
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You can learn more about prompt engineering with this short video, watch now!
FAQs
1. Is Prompt Engineering hard to learn?
Learning prompt engineering can be challenging due to its interdisciplinary nature. It requires technical AI understanding, linguistic skills, and creativity. However, dedication and the right resources make it accessible to those committed to mastering it.
2. Do you need to know coding to become a prompt engineer?
Yes, a basic understanding of coding, particularly in languages like Python, is beneficial for prompt engineering. It helps interact with AI models and automate tasks.
3. What are the advantages of prompt engineering?
Prompt engineering optimizes AI interactions, improving the accuracy and relevance of AI outputs. It enhances user experiences across various applications, making AI technologies more effective and accessible.
4. Can I Practice Prompt Engineering Without Access to AI?
Yes, you can start by understanding the principles of effective communication and command structuring. Analyzing existing AI outputs and theorizing how different prompts might alter them is also beneficial.
5. What Industries Need Prompt Engineers?
Many industries, including technology, healthcare, education, content creation, customer service, and entertainment, need prompt engineers to refine AI interactions and outputs for specific applications.
Books, Courses & Certifications
Complete Guide with Curriculum & Fees
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.
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.
Books, Courses & Certifications
Teaching Developers to Think with AI – O’Reilly
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 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.
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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