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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallAn AI coding assistant can fix a bug and still fail the task: it may use a method you prohibited, return the wrong format, or change code outside the requested scope. To judge whether it followed a prompt, measure two things separately: whether it solved the underlying problem and whether it obeyed each material instruction.
Why a correct answer can still be wrong for the task
Task correctness and instruction compliance are related, but they are not the same. A patch can produce the right behavior while breaking a constraint the user explicitly set. For example, a fix that works but uses a forbidden JavaScript method is technically successful at the bug and noncompliant with the request.
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The distinction also applies beyond coding. A response may contain the right facts but ignore a requested format, style, or structure. A 2026 EACL paper describes task accuracy as the factual correctness of the core output, and instruction following as adherence to meta-rules about format, style, or structure. Its evaluation found that compliance varied by constraint type, quantity, and position; it does not establish a universal failure rate for all models or tasks. Read the MOSAIC paper.
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Turn the prompt into a short list of requirements that can be checked independently. For a coding request, that might include the programming language, a prohibited method, the required output format, and the boundary of permitted changes.
#1 Best Overall
- Task result: Does the code actually fix the stated problem?
- Required language: Is the solution written in the language requested?
- Prohibited methods: Did it avoid each disallowed function or technique?
- Output format: Did it return only what was requested, such as corrected code without an explanation?
- Scope: Did it avoid unrelated edits?
Mark each requirement as met or unmet, and define what counts as a violation before comparing responses. For code, reviewing the diff helps identify changes beyond the requested fix. Keep the task result and compliance record separate: a single pass/fail label can hide a useful fix that still broke an important constraint.
What a compliance score does—and does not—tell you
Suppose a prompt has four requirements and a response follows three. That is 3 of 4, or 75% instruction compliance. It is an illustrative calculation, not a measured result for a particular AI model. It also says nothing by itself about whether the code works: task correctness needs its own assessment.
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An aggregate percentage can make comparisons easier, but it can hide which instructions models struggle with. Results are more informative when they show task correctness and each constraint category separately, along with the constraints’ number and placement. MOSAIC’s reported variation by constraint type, quantity, and position is a reason to disclose those details—not evidence that every coding evaluation will show the same pattern.
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What the proposed coding benchmark establishes
Akanksha Sharma’s article, “The AI Was Right. The Answer Was Still Wrong.”, proposes giving the same prompt to multiple models, checking whether each solves the main task, and assessing every instruction separately. Its example asks for a JavaScript fix, forbids map(), and requests only corrected code.
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The article presents a way to think about evaluation, not a completed benchmark with published comparative findings. It does not report the task-set size, tested model names or versions, repeated-run method, or aggregate outcomes. The example’s 75% figure is a worked illustration, not evidence that any named model achieved that score or that one model outperforms another.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to make a model comparison more useful
A fair comparison needs more than the same prompt: it needs clear scoring rules and enough reporting detail to interpret the result. At minimum, publish:
- the prompts and task set, including every constraint being evaluated;
- the models and versions tested, and when they were run;
- how task correctness and each instruction violation are defined;
- whether prompts were repeated, and how repeated runs were summarized;
- results split by constraint type and placement, not only one overall score.
Those details help distinguish a model that solves the coding problem but disregards a format rule from one that follows the rules but fails to fix the bug. They also prevent a score from being treated as a broad guarantee about performance on different tasks.
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