The AI Was Right. The Answer Was Still Wrong.

Have you ever asked AI to do something very specific and it did almost everything right?

Except the one thing you specifically told it not to do.

That made me curious.

Instead of asking “Which AI model is smartest?” I wanted to test something much more specific:

Can AI actually follow small instructions while solving a task?

What I Benchmarked

I created a benchmark focused on instruction following in coding tasks.

For example:

Fix this JavaScript function.
Don't use map().
Return only the corrected code.

The model isn't only expected to fix the bug.

It also needs to follow every instruction.

So I check:

• Did it solve the problem?
• Did it use the requested language?
• Did it avoid the prohibited method?
• Did it follow the required output format?
• Did it change anything that wasn't requested?

Because sometimes the answer is correct but the task isn't.

How I Tested the Models

The testing flow is simple:

Same prompt → Multiple models → Same evaluation → Compare

For every task, I:

1. Give the exact same prompt to each model.
2. Collect their responses.
3. Check whether the main problem was solved.
4. Check each instruction separately.
5. Record which instructions were followed or violated.
6. Compare the results across models.

This keeps the test focused on the model's behavior rather than changing the task for each model.

How I Measured It

I didn't want a simple right/wrong score.

A response can solve the coding problem while still breaking one of the instructions.

So I measure two things separately:

1. Task Correctness
Did the model actually solve the underlying coding problem?

2. Instruction Compliance
Did it follow the individual constraints in the prompt?

For example if a task contains four requirements and the model follows three:

3 / 4 = 75% instruction compliance

Across the benchmark these scores can show whether a model is not only capable of solving the problem but also capable of doing exactly what was asked.

Why This Interested Me

AI is becoming a bigger part of everyday coding.

We often focus on whether a model can generate working code but real developer tasks usually come with constraints:

“Don't change this.”

“Use this approach.”

“Return only the code.”

“Don't use this method.”

Those tiny instructions can completely change whether the response is actually useful.

That's the behavior I wanted to investigate.

What I'm Looking For

The interesting part isn't simply finding a model with the highest number.

I'm looking for patterns.

Do models struggle more when there are several constraints?

Does solving the code correctly make instruction following more reliable?

Which types of instructions are most commonly ignored?

And can a model be technically right while still being practically wrong?

Those are the questions this benchmark is designed to explore.

What I'd Test Next

If I expand the benchmark I'd add:

• Multi-turn instructions
• Longer prompts
• More constraints in a single task
• Conflicting instructions
• Ambiguous instructions
• More complex code editing tasks
• Self correction after an instruction following mistake

That could reveal whether instruction following remains reliable as tasks become more realistic.

Your Turn

Now I'm curious about your experience.

What's the smallest instruction you've given an AI that it completely ignored? 😭

Drop it in the comments.

Maybe your most frustrating AI moment becomes the next benchmark task.

Comments

[dougame]

This is such a relatable problem! AI gives you a confident-sounding answer that's technically correct but misses the actual point.

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