Forbid two libraries and see what your AI is actually made of

Try this right now: open a fresh chat and ask your AI to write a Python data-processing script. Any script. Normal request, no special sauce. Watch how fast it reaches for Pandas or NumPy.

That’s the default. And it’s fine, until you actually need to understand the code, run it somewhere restrictive, or trust it in production. The library does the heavy lifting and the AI just fills in the blanks around it. You get working code. You don’t necessarily get insight into whether the AI could have solved the problem without the shortcut.

🔒 The Constraint-Gate Idea

High-level libraries like Pandas and NumPy are powerful. But they’re also abstractions. They let the AI write code that looks functional without necessarily revealing whether the AI understands the underlying logic.

Think of it like asking a chef to cook without a food processor. You find out fast whether they actually know knife technique or if they’ve just been hitting a button. The meal might look the same on the plate. The process tells you everything.

Take the scaffolding away and you get to see the architecture. What’s actually holding the thing up.

Here’s the exact prompt the author shared:

Write this Python script but do NOT use ‘Pandas’ or ‘NumPy’. Use only standard libraries.

That’s four extra words and one constraint. The output that comes back is a completely different kind of code. More explicit. More transparent. Sometimes more interesting. And critically, more auditable. You can read it line by line without needing to know what .groupby() is doing under the hood.

🔧 How to Run the Test

1. Pick any Python task: CSV parsing, data filtering, number crunching, whatever.
2. Write your normal request, nothing fancy.
3. Add this at the end: “Do NOT use Pandas or NumPy. Use only standard libraries.”
4. Run the default version and the constrained version side by side.
5. Compare. Not to declare a winner, but to see what each version reveals.

You’re not trying to prove that standard libraries are better. You’re using the constraint as a diagnostic. Like asking someone to explain a concept without using the textbook definition. If they can do it, they actually know the material. If they can’t, the textbook was doing the knowing for them.

One thing worth doing: run this test on a task you already understand well. That way you can actually evaluate whether the constrained output makes sense or just looks like it does. The test is most useful when you have a reference point.

📊 What You’re Actually Looking For

Constrained version looks clean and logical: Good sign. The AI understood the problem, not just the library syntax. Code like this travels well. It runs in older Python environments, restricted servers, embedded systems, anywhere you can’t install dependencies. It’s also easier to hand to a junior developer or non-engineer who needs to follow the logic step by step.

Constrained version gets messy or falls apart: Also useful information. The AI was leaning on the library to do the thinking. The loops get awkward, the variable names get vague, the logic gets tangled. Worth knowing before you ship anything to production or hand it to someone else to maintain. That messiness is the real signal.

Constrained version surprises you: This happens more than you’d expect. Without Pandas in the picture, the AI might reach for csv.DictReader, collections.Counter, or a generator pattern you’d never have thought to ask for. Sometimes constraints produce creativity. The AI stops pattern-matching to the obvious solution and actually reasons through the problem from scratch. That’s the version worth saving.

💡 Extra Tips

• Swap the constraint for your actual stack: “no requests, use urllib only” or “no datetime, parse timestamps manually.”
• Add inline comments to the constraint: “explain each step of the logic as you go.” Turns the output into something you can actually learn from, not just copy.
• Works outside Python too. Try it with JavaScript: “no lodash, no moment.js” forces vanilla logic every time.
• Use constrained output as onboarding material. It’s a better teaching artifact than library-heavy code when you’re trying to explain what something actually does.
• If the constrained version is good, ask the AI to now rewrite it with Pandas added back. You’ll understand every line of the Pandas version because you already saw what it’s replacing.

✍️ Prompt of the Day

Write this Python script but do NOT use ‘Pandas’ or ‘NumPy’. Use only standard libraries. Add an inline comment for each step explaining the logic.

Paste that after any data processing request and the output changes completely. You stop getting a library call. You start getting an explanation disguised as code. Every line tells you what it’s doing and why, which means you can actually review it, modify it, and own it instead of just running it and hoping for the best.

The technique is live in the original thread on r/PromptEngineering if you want to see how others are extending it. Go check it out 👇

The ‘Constraint-Gate’ for coding complex scripts.
by u/Significant-Strike40 in PromptEngineering