Everyone is learning how to prompt AI. Far fewer people are learning how to challenge its answers.
That may become a much bigger issue than we realize.
AI is getting remarkably good at producing answers that sound correct.
The writing is polished.
The explanation is confident.
The code looks professional.
The reasoning appears logical.
Sometimes the answer is completely right.
Sometimes it isn’t.
The difficult part is that both answers can sound equally convincing.
That leads to a skill I think deserves much more attention:
Knowing how to verify what AI gives you.
We have spent a lot of time learning how to talk to AI.
Now we need to get better at questioning it.
Prompting Is Only Half the Skill
A lot of AI education currently focuses on prompts.
How do I write a better prompt?
Should I give the AI a role?
How much context should I provide?
Should I include examples?
How do I get a more detailed response?
These are useful skills.
But there’s another question that becomes increasingly important as AI gets better:
How do I know whether the answer deserves to be trusted?
That’s a very different AI skill.
And I think it will become one of the defining skills of working effectively with AI.
Confidence Is Not Evidence
Imagine asking an AI:
“What’s wrong with this JavaScript code?”
Within seconds, it gives you an explanation and rewrites part of your application.
The explanation sounds reasonable.
You paste the code.
It works.
Success?
Maybe.
What if the change introduced another bug?
What if it fixed one situation while breaking an edge case?
What if the AI misunderstood your application’s data flow?
What if the solution works today but creates a maintenance problem later?
This is why I don’t think our workflow should be:
AI generated it → therefore I’m finished.
A much stronger workflow is:
Generate → Question → Inspect → Test → Verify → Improve
AI can accelerate the work.
But acceleration doesn’t eliminate the need for judgment.
AI Creates an Interesting Paradox
AI makes it easier to do things we don’t completely understand.
That’s incredibly powerful.
It’s also exactly why understanding becomes more valuable.
Before generative AI, creating something often forced you through a learning process.
You searched.
You read documentation.
You experimented.
You made mistakes.
You debugged.
You tried again.
Eventually you arrived at an answer.
That journey gave you knowledge that helped you evaluate the result.
AI can compress much of that process into seconds.
But there’s a catch.
The answer gets compressed.
And sometimes…
the learning gets compressed too.
If AI writes 300 lines of JavaScript for you in 30 seconds, that’s impressive.
But here’s the more interesting question:
Could you recognize the bug hiding inside those 300 lines?
That’s where AI literacy becomes important.
Try My 5-Question AI Test
The next time AI gives you an important answer, don’t immediately accept it.
Ask these five questions.
1. What assumptions did you make?
Every prompt leaves something out.
AI often fills those gaps automatically.
Try:
List the assumptions you made when producing this answer.
You may discover that the AI assumed something very different from what you intended.
2. What might be wrong?
Try:
Identify the three parts of your answer most likely to be incorrect, incomplete, or misleading. Explain why.
You’ve now changed the AI’s role.
Instead of simply generating an answer, you’re asking it to critique one.
3. What evidence would verify this?
Ask:
What evidence, documentation, test, or source would help verify the major claims in this answer?
This changes the question from:
“Does this sound right?”
to:
“How could I demonstrate that this is right?”
That’s a much stronger question.
4. Can I make it fail?
Developers already understand this concept.
Don’t only test the happy path.
Try to break it.
Instead of asking:
“Does this code work?”
try:
“Create tests specifically designed to expose weaknesses, edge cases, and incorrect assumptions in this code.”
Now AI isn’t just your code generator.
It’s helping you become the reviewer.
5. What happens if this answer is wrong?
Not every AI response requires the same level of verification.
If you’re brainstorming names for a fictional project, an imperfect answer probably doesn’t matter very much.
If you’re relying on an answer for something important, the standard should be much higher.
I think of this as a trust budget:
Higher consequences → More verification
That’s a useful rule whether you’re working with AI-generated code, research, analysis, business information, or everyday answers.
Here’s a Prompt Worth Saving
After receiving an important AI response, try this:
Act as a skeptical reviewer of your previous response. Identify unsupported assumptions, possible errors, missing information, and claims that require independent verification. Separate facts from inference and suggest specific ways I can test or verify the important parts of the answer.
Notice what happens.
The original interaction was:
Prompt → Answer
Now it becomes:
Prompt → Answer → Challenge → Evidence → Verification
That’s a fundamentally different way of working with AI.
Build Verification Into the Original Prompt
We can take this idea one step further.
Why wait until after AI generates an answer to think about verification?
Build verification into the prompt from the beginning.
I’ve created a free AI Prompt Generator & Writing Guide on DiscoveryVIP that can help you structure better prompts.
Try the AI Prompt Generator & Writing Guide:
https://www.discoveryvip.com/tool.php?slug=ai-prompt-generator
Instead of writing:
Review my JavaScript.
Think about defining:
Goal → Expertise → Audience → Context → Requirements → Boundaries → Output → Evidence → Success Criteria
Then include instructions such as:
Identify your assumptions.
Flag uncertainty.
Separate evidence from inference.
Explain possible weaknesses.
Suggest tests.
State what should be independently verified.
Now you’re not simply writing a better prompt.
You’re designing a better thinking process.
Try This Experiment
Here’s something you can try in about ten minutes.
Start with a question you would normally ask AI.
Round 1 — Ask Normally
Use your typical one- or two-sentence prompt.
Save the answer.
Round 2 — Structure the Prompt
Use my free AI Prompt Generator:
https://www.discoveryvip.com/tool.php?slug=ai-prompt-generator
Add more context.
Define your goal.
Add requirements.
Set boundaries.
Specify your desired output.
Include evidence and verification requirements.
Then run the new prompt through your preferred AI.
Round 3 — Challenge the Answer
Take the result and ask:
What assumptions did you make, what could be wrong, and how can I independently verify the important parts of this answer?
Now compare the results.
But don’t simply ask:
Which answer sounds better?
Ask:
Which answer gives me more reasons to trust it?
That’s the experiment.
If you try it, I’d be interested to hear what changes you notice.
Developers Already Know Why This Matters
There’s a principle developers learn fairly quickly:
Working code isn’t necessarily good code.
Code can run successfully and still be poorly structured, inefficient, difficult to maintain, unable to handle edge cases, or based on incorrect assumptions.
AI-generated code doesn’t eliminate those problems.
In some situations, it can simply produce them faster.
That’s one of the reasons I’ve spent so much time exploring Vibe Coding and AI-assisted development.
The objective isn’t:
Let AI write everything.
A better objective is:
Use AI to accelerate development while keeping humans involved in understanding, testing, debugging, reviewing and deciding.
I’ve created several free Vibe Coding and development learning guides around these ideas.
You can explore all of my interactive guides here:
https://discoveryvip.com/guides.php
Context May Matter More Than the Prompt
There’s another reason AI sometimes gets things wrong.
The AI may understand JavaScript.
It may understand APIs.
It may understand CSS.
It may understand software architecture.
But it doesn’t automatically understand your project.
It doesn’t necessarily know your users.
Your existing architecture.
Your previous decisions.
Your constraints.
Your definition of success.
Your unusual edge cases.
That’s why providing context becomes so important.
A mediocre prompt with excellent context can sometimes be more useful than a clever prompt with almost no context.
This is where prompting begins to evolve into something broader:
Context engineering.
The question isn’t simply:
How do I ask AI a better question?
It becomes:
How do I give AI the information it needs to reason about my specific problem?
Maybe Prompt Engineering Is Evolving
I think we’re already seeing several generations of AI prompting.
Generation 1
Create this.
Generation 2
Create this using these requirements.
Generation 3
Create this using these requirements and this context.
And now we’re moving toward something more interesting.
Generation 4
Create this using the available context, identify your assumptions, expose uncertainty, challenge the result, test it, and show me how to verify it.
That’s a very different relationship with AI.
We’re moving from:
AI as an answer machine
toward:
AI as collaborator + critic + tester + thinking partner
But there is one condition.
We can’t outsource the final judgment.
Learn by Building, Not Just Reading
This is also why I’m a big believer in learning AI through experimentation.
Reading about prompting is useful.
Actually changing a prompt and seeing what happens is better.
Reading about hallucinations is useful.
Trying to expose one yourself is better.
Reading about AI-assisted coding is useful.
Building something, breaking it, debugging it and improving it is better.
I’ve created a growing collection of free interactive guides on DiscoveryVIP covering topics including Vibe Coding, Vibe Learning, JavaScript, Google Apps Script, HTML, CSS, web design, website testing and AI-assisted development.
Explore the free interactive guides:
https://discoveryvip.com/guides.php
You can also experiment with AI concepts in my AI Learning Studio, which includes guided lessons, experiment labs and practical projects.
AI Learning Studio:
https://discoveryvip.com/ai-learning-studio
The goal isn’t simply to learn more information about AI.
It’s to become better at working with AI.
Here’s the Question I Want LinkedIn to Debate
We’re teaching millions of people how to use AI.
We’re teaching people how to prompt AI.
We’re teaching people how to build with AI.
But are we spending enough time teaching people how to:
Question AI?
Because as AI becomes more capable, the valuable skill may not simply be getting an answer faster.
It may be knowing:
when to trust it,
when to challenge it,
when to test it,
and
when to reject it.
So I’m curious where you stand.
Which skill becomes most valuable in an AI-first world?
A — Prompting skills
B — Verification and critical thinking
C — Deep subject expertise
D — Knowing how to combine all three
And here’s the question I’m even more interested in:
Which one are we currently neglecting the most?
Share your answer and reasoning in the comments. I think this is a conversation we need to have as AI becomes part of more of our everyday work.
Free Resources
AI Prompt Generator & Writing Guide
https://www.discoveryvip.com/tool.php?slug=ai-prompt-generator
DiscoveryVIP Interactive Learning Guides
https://discoveryvip.com/guides.php
AI Learning Studio
https://discoveryvip.com/ai-learning-studio
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