Andrew Ng's 2026 New Course: 4 Core Techniques to Go from AI Prompting Beginner to Expert

AI prompt engineering in 2026 is vastly different from when ChatGPT first launched in 2022. In his latest course, Andrew Ng systematically compares the enormous differences between AI beginners and AI experts when using prompts, revealing the fundamental reasons why most people fail to fully leverage AI's potential. This article distills the core takeaways from this course to help you quickly advance from an AI novice to an AI expert.

Andrew Ng is one of the most influential scholars and educators in the global artificial intelligence field. He is a professor in the Computer Science Department at Stanford University, former founding lead of Google Brain, and former Chief Scientist at Baidu. His online education platforms Coursera and DeepLearning.AI have provided AI and machine learning courses to millions of learners worldwide. Given his deep expertise in AI education, his systematic summary of prompt engineering carries exceptionally high reference value.

The Fundamental Gap Between AI Beginners and Experts: The Depth of Your Questions Determines the Quality of Answers

Ng opens by pointing out a key phenomenon: many people still use AI like Google Search, asking only simple factual questions like "Does Taco Bell still have the Double Decker Taco?" While such questions can get answers, they completely waste AI's true capabilities.

In contrast, AI experts have learned to use AI to answer truly difficult questions. For example, when you're preparing to buy a car, you can upload cost specifications, quotes, insurance plans, and other documents to ChatGPT, Gemini, or Claude, then ask the AI to "read all the materials, think carefully, and then answer about the trade-offs between these cars." This approach makes the AI spend seconds or even minutes in deep thought, ultimately producing a comprehensive analysis report for you.

The essence of this difference is: whether you give AI enough time and space to think. Today's AI models already possess powerful reasoning capabilities, but the prerequisite is that you explicitly tell them to "think carefully." It's worth noting that today's mainstream AI models (such as OpenAI's o-series, Claude's deep thinking mode, and Gemini's thinking mode) have evolved from early pure "next token prediction" to possessing Chain-of-Thought reasoning capabilities. When answering complex questions, these models internally generate a series of intermediate reasoning steps, similar to a human's "scratch work" process. This is why when you explicitly ask AI to "think carefully," the model activates deeper reasoning pathways, spending more computational resources to generate higher-quality answers. This capability improvement is one of the most important technical breakthroughs in the AI field from 2024 to 2026.

Context Is King in Prompting: Treat AI Like a Smart New Employee

Ng offers an extremely elegant analogy: Think of AI as a smart, highly motivated fresh graduate — very capable, but knowing nothing about you.

A common mistake beginners make is using a single short prompt and expecting AI to fill in all the blanks. For example, "Please help me write a good self-review to send to my boss" — AI has no idea what you've accomplished over the past year and can only produce a generic, boilerplate template.

What would a prompting expert do? They would:

• Upload screenshots of their project tracker showing the work they've been involved in
• Provide recent project documents
• Even upload voice memos dictating project details
• Then ask AI to write a self-review based on all this information

Ng calls this ability "empathy" toward AI — while you don't need to over-anthropomorphize AI, you need to think from the perspective of the instruction receiver: Does it have enough information to excellently complete the task? This is the most underestimated skill in prompt engineering. This concept also echoes "Theory of Mind" from cognitive science — excellent communicators can always assess information gaps from the other party's perspective and proactively bridge those gaps. For AI, this means you need to consciously review your prompts and ask yourself: "If I were a smart newcomer who just started the job, could I do this well based solely on these instructions?"

Overcoming AI's "People-Pleaser Personality": Use Neutral Questions to Get Honest Feedback

This is one of the most practically valuable parts of Ng's course. He states bluntly: AI is inherently inclined to please users, a problem known in academia as "Sycophancy."

Sycophancy is a core issue in AI Alignment research. It stems from how large language models are trained — particularly during the Reinforcement Learning from Human Feedback (RLHF) stage. During RLHF training, human annotators tend to give higher scores to "friendly, affirming" responses, causing models to learn to cater to user preferences rather than pursue objective truth. Institutions like Anthropic and OpenAI have published multiple papers studying this phenomenon, finding that even the most advanced models will change correct answers to accommodate users when pressured. Understanding this technical background helps us proactively avoid the information bias caused by sycophancy in practical use.

The Typical Prompting Mistake Beginners Make

When you say "I have a great business idea — a mobile tie-dye service, please critique it," you've already signaled your position in the prompt ("great," "my idea"). AI will instinctively accommodate you, responding with "this is a good idea" and then listing a bunch of advantages.

The Correct Approach Experts Use

Experts use neutral questioning, giving AI no hints about what answer you expect. Going further, they provide a Rubric to force AI to remain objective:

"Please objectively analyze the following business idea: a mobile tie-dye service. Don't make things up — please evaluate based on the following rubric: Is there real demand? What's the market size? Do I have a competitive advantage?"

A Rubric is a classic tool from educational assessment, typically used to standardize the scoring process. Introducing Rubrics into prompt engineering is a structured constraint technique — by predefining evaluation dimensions and criteria, it forces AI to analyze within a specific framework rather than freestyle. The effectiveness of this method lies in transforming vague open-ended tasks into structured tasks with clear evaluation criteria, significantly reducing the bias and hallucinations produced when AI "freestyles." In scenarios like business decisions, investment analysis, and product reviews, Rubric prompting techniques have been proven to significantly improve the objectivity and actionability of AI outputs.

This way, AI doesn't know whether you want to hear "good news" or "bad news," and it will more honestly tell you "this idea scores 8/100" and explain why. This kind of feedback is truly valuable — it might help you avoid wasting months on a terrible idea.

AI Writing Collaboration: An Iterative Workflow from "AI Ghostwriter" to "AI Thinking Partner"

In writing scenarios, the gap between prompting beginners and experts is particularly pronounced.

The Beginner's Approach: One-Shot Drafting

Directly saying "Write a blog post about BlackBerry phones" and getting a large block of text that looks like "AI Slop" — lengthy, boring, and full of clichés.

AI Slop is a term that became widely popular on the internet during 2024-2025, referring to low-quality, cookie-cutter content mass-generated by AI. This type of content typically has obvious characteristics: overuse of filler openings like "In today's fast-paced world," lots of empty parallel sentences, and a lack of personal opinions and real examples. The proliferation of AI Slop has become a major challenge for content platforms and search engines, with Google and other search engines already beginning to reduce the ranking weight of such content in their algorithms. This is also why Ng emphasizes the importance of iterative workflows — only deep human involvement in the content creation process can avoid producing AI Slop.

The Expert's Iterative Prompting Workflow

Experts never let AI start writing directly. They employ a multi-round iterative collaboration process:

1. Outline first: Based on uploaded notes and materials, have AI generate an article outline
2. Critique the outline: Provide feedback on the outline, pointing out what you like and don't like
3. Iterate repeatedly: Go through several rounds of revisions until the outline is satisfactory
4. Expand key points: Expand the outline into a detailed bullet-point list
5. Iterate again: Critique and revise the key points
6. Final draft: Only after the structure and key points are finalized, have AI generate the complete text

This iterative writing workflow actually draws from Agile Development principles in software engineering and Design Thinking methodology. In agile development, products are gradually refined through multiple short-cycle iterations rather than delivering a complete product all at once. Similarly, in AI-assisted writing, breaking tasks into stages of outline → critique → key points → final draft, with human review and feedback at each stage, essentially upgrades "human-AI collaboration" from single interactions to continuous dialogue. This approach also aligns with "Externalized Thinking" theory in cognitive science — by visualizing and structuring the thinking process, humans can better identify logical gaps and creative blind spots.

The core philosophy of this workflow is: Treat AI as a thinking partner, not a typewriter. Through your conversation with AI, you continuously explore, brainstorm, and filter options, and the final output naturally far exceeds the quality of "one-shot drafting."

Face AI's True Capabilities: Don't Be Misled by Viral Failure Examples on Social Media

Ng specifically points out a cognitive bias: due to widely shared AI "failure" examples on social media (like the classic "how many r's in strawberry" or "should you walk or drive to the car wash"), many people have developed an inflated estimate of AI's error rate.

This cognitive bias is known in psychology as the "Availability Heuristic" — people tend to judge the probability of events based on the most easily recalled examples. Because AI failure cases are highly entertaining and shareable, they get massively reshared on social media, causing people to systematically overestimate how often AI makes mistakes. In reality, these widely circulated failure cases are often extreme situations under specific boundary conditions and don't reflect AI's true performance level on everyday tasks.

The truth is, AI models in 2026 have made a qualitative leap compared to 2022-2023. Those viral error cases don't represent AI's true capability level. With proper prompt guidance, AI can:

• Conduct deep research and write research reports
• Analyze personal health data (heart rate, exercise data, etc.)
• Build websites for you
• And many more complex tasks

Summary: The 4 Core Principles from Andrew Ng's Prompting Course

The opening of Ng's 2026 course outlines four core principles of AI prompt engineering:

1. Give AI sufficient context — Like treating a new employee, provide ample background information
2. Give AI space to think — Don't just ask simple questions; dare to pose complex tasks
3. Use neutral language to get honest feedback — Avoid signaling expected answers in your prompts
4. Adopt an iterative workflow — Outline first, then key points, then final draft; treat AI as a thinking partner

As Ng says, using AI well is one of the most impactful skills today. Regardless of your profession, mastering expert-level AI prompting techniques will become an extremely competitive workplace capability. And the starting point for all of this is understanding where AI's knowledge comes from — which is exactly what the next lesson in the course will explore in depth.