Code from description: how to evaluate the result quality
The "Code from description" tool transforms a textual formulation of a task into a small program with usage instructions, explicitly stated assumptions and no automatic execution. This means you get source text that you need to read, check and run yourself. The main skill here is not writing a prompt, but properly evaluating what the system returned. In this guide we will examine how to check accuracy, completeness and suitability for a specific example, what limitations to consider and how to distinguish a working fragment from a plausible but incorrect one.
Ouvrir l'outil: Générateur de code ↗What the tool actually returns
At output you get a text file with code, comments and, usually, a short launch instruction. The tool does not execute the program, does not install dependencies and does not check it on your machine. Therefore the responsibility for running and checking lies with you.
It is important to understand the input format: you fill in a task description field, specify a platform or application and a goal. The more specific these three points are, the fewer guesses the model has to make. If the platform is not specified, the result may end up in a different language or for another environment.
The result is saved as plain text TXT, it can be copied or downloaded. There is no integration with an editor, auto-saving or batch processing, so checking is entirely manual.
Example request: "Write a Python function that removes duplicates from a list, preserving order. Platform: Python 3.11. Goal: educational example for students."
Accuracy: does the code match the description
First compare the code's behavior with your formulation. Take key words from the request — "removes duplicates", "preserving order" — and check that exactly that and does the proposed function do it. A common mistake: code removes duplicates, but changes the order, e.g. by converting to a set.
Check edge cases that you imply but did not write. What will happen with an empty list, a list with one element, with non-hashable objects? If the description did not include this, the model could choose any behavior. This is not necessarily an error, but an assumption that needs to be fixed.
Separately look at data types and language version. A function written for Python 3.11 may not work in 3.8 if new syntactic possibilities are used. Accuracy is not only logic, but also compatibility with the environment you specified.
Check: duplicates([3,1,3,2,1]) should return [3,1,2]. If it returned [1,2,3] or [3,1,2] with changed type, accuracy was violated.
Completeness: is everything needed present in the result
Completeness means that the code has all parts necessary to run: imports, function definition, example call, error handling, if claimed. The tool promises instructions and stated assumptions — check that they are actually present, not just mentioned in one sentence.
Ask yourself if you can run this file as is. If a entry point, dependencies or test example are missing, add them yourself. Lack of a call example is a common problem: the code looks finished, but it is unclear how to check it.
Pay attention to exception handling. If the task implies working with user input or files, and the code has not a single check, that is a gap. Not necessarily requiring industrial reliability, but basic checks must match the stated purpose.
Full result for an educational task: function, docstring, example call and output. If there is no example, add it yourself before running.
Suitability: does the code suit your task
Suitability is correspondence not only to the text, but also to the context. An educational example for students and a production script for data processing require different levels of rigor. The same code may be accurate and complete, but unsuitable due to complexity, style or dependencies.
Check readability: variable names, comments, structure. For learning clarity is important, for a quick prototype — minimum superfluous. If the code uses an unfamiliar library, evaluate whether you are ready to install and maintain it.
Make sure the solution does not pull in unnecessary dependencies. Sometimes it is simpler to rewrite a function using the standard library than to add a package for one operation. Suitability is a balance between accuracy and maintenance cost.
A 40-line function with recursion and an external library to remove duplicates from a list of 10 elements is excessive for an educational task.
How to check the result step by step
Start by reading the code in its entirety, without running it. Mark places where behavior is not obvious, and assumptions the model made for you. Then run the code on a small example that you invented beforehand, not on the one provided in the result.
Compare the actual output with the expected. If there is a discrepancy, determine its cause: incorrect logic, incorrect type, incorrect language version or incorrect interpretation of the task. Then formulate a refinement and generate the result again.
Check edge cases separately: empty input, large input, incorrect input. Fix what the code does in each case and decide if it is acceptable for your goal. Such a checklist takes a few minutes, but saves hours of debugging.
Checklist: 1) read the code; 2) run on your own example; 3) check empty list; 4) check order; 5) compare with expectation; 6) if discrepancy, refine prompt.
Tool limitations and typical errors
The tool does not execute code and does not guarantee its functionality. The model can make mistakes, and availability and response queue may change. Therefore the result should always be considered a draft, not a finished solution.
Among typical errors: incorrect handling of edge cases, unnecessary dependencies, code for another language version, missing call example, style mixing. Also plausible but incorrect comments that describe not what the code does are often encountered.
Do not expect integration with an editor, auto-saving or batch processing — these features are not available. Export is available only as plain text. Take this into account when planning work and do not rely on the tool where automatic checking is needed.
If the code references a library that is not in your environment, this is not a tool error, but a result limitation that must be manually resolved.
Practical example of evaluation
Let's take a task: "Write a Python function that removes duplicates from a list, preserving order." Suppose the tool returned a function that uses a set to track seen elements and adds an element to the result only at its first appearance. This is an exact and complete solution for hashable elements.
Check on an example [3,1,3,2,1]: expected result [3,1,2]. If the function returns exactly that and does not change type, accuracy is confirmed. Completeness: is there a docstring, a call example, handling of an empty list? If an empty list returns an empty list, this is correct.
Suitability: for an educational solution on five lines is suitable. For a list with non-hashable objects it will not work, and this limitation must either be accepted or the request refined. Such an analysis shows how one and the same function can be accurate, complete and suitable in one context and unsuitable in another.
Expected output: [3, 1, 2]. If received [3, 1, 2] and an empty list gives [], the result can be considered correct for the stated conditions.
Evaluating code generated from a description boils down to three questions: does the code do what you asked for, is everything needed to run it present, and does it suit your task? The "Code from description" tool gives a draft with instructions and assumptions, but does not execute the code and does not guarantee the result. Check accuracy on your own examples, completeness by the presence of runnable parts, and suitability by the context. Such an approach turns generation into a manageable process, where you remain the one making decisions about quality.
Ouvrir l'outil: Générateur de code ↗Les traductions de l'interface sont assistées par machine et peuvent nécessiter des améliorations.