Code documentation writer: how to craft an accurate prompt
The Code documentation writer tool creates textual descriptions of provided functions: parameters, return values, examples and limitations. The output quality depends on how accurately the prompt is formulated. In this article we will explore what to write in the input fields, how to distinguish a good prompt from a bad one, how to check the generated text and what limitations are important to keep in mind. All code examples and prompts in the article are educational illustrations, not results of actual runs.
Open tool: Code documentation writer ↗What the tool does and why phrasing matters
"Code documentation writer" is a web form with text fields. You paste a code snippet, optionally add a note, select the output language and press the generate button. The ready result appears in a separate panel: it can be copied or downloaded as a plain text TXT file. The tool does not execute code and does not check it for errors — it only compiles a description based on the text you provide.
The quality of the description depends directly on the input data. If you insert only a function signature without explanations, the tool may incorrectly determine the purpose of parameters, invent unnecessary details or miss important limitations. The more specifically you describe what the function does and what exactly should be reflected in the documentation, the closer the result will be to the expected outcome.
Keep in mind that the tool works only with text. It does not upload files, does not connect to repositories and does not save request history. Everything that should get into the documentation must be described in words in the input.
Educational illustration. Input: def add(a, b): return a + b. Explanation: 'Describe parameters as numbers, return — sum, add an example call'. Such input yields a more predictable result than a single line of code without explanations.
Input fields and their purpose
The form contains two fields. The first — 'Code and error message' — is intended for the code fragment itself and brief explanations of it. The maximum length of this field is 8000 characters. The second field — 'Platform or application' — sets the context: programming language, version, usage environment. Its length is limited to 1000 characters.
It is useful to add one or two phrases about the function's purpose in the code field: what data types are expected, what is returned on error, are there any special cases. This is not a mandatory requirement, but such details help the tool formulate the description more accurately and avoid guessing behavior.
The 'Platform or application' field influences the style of examples and terminology. If you specify Python 3.9 and a beginner tutorial example, the description will be simpler than if you specify, say, an industrial web service on JavaScript. Context helps the tool pick appropriate formulations.
Educational illustration. Field 'Code': def add(a, b): return a + b. Field 'Platform': Python 3.9, beginner tutorial example.
How to build a good prompt
A good prompt usually includes four elements. Context — where and how the function is used. Purpose — what exactly needs to be documented: parameters, return value, example, limitations. Format — in what form to present the result; the tool outputs only plain text, so it is worth deciding in advance whether a list or a connected paragraph is needed. Limitations — what should not be mentioned or which details are mandatory.
For example, if a function is intended only for internal use, indicate this so that the description does not contain examples for external users. If the function can raise an exception, ask to describe the condition under which this happens.
The more specific the prompt, the less likely the tool will add unnecessary things or miss important details. General phrases like 'do well' carry no meaning and do not help. It is better to spend a few seconds on clarification than to rewrite the result later.
Educational illustration. Context: the add function is used in a calculator. Purpose: describe parameters a and b, return value, example call. Format: plain text. Limitation: do not mention other functions.
Comparing poor and good prompts
Let us consider a Python function for illustration: def add(a, b): return a + b. A poor prompt — just 'Document it'. In response the tool may give a general description without indicating types and without an example. A good prompt contains clarifications: 'Describe the add function: parameters a and b are numbers, returns a sum. Provide an example add(2, 3) -> 5. Note that passing strings results in concatenation. Platform: Python 3.9'.
In the good version it is explicitly stated that the parameters are numbers, but the string feature is also noted. This helps the tool create a more complete description. If strings are not mentioned, the documentation may turn out incomplete.
Specifying the platform is also important: without it examples may not match Python syntax. Context sets the expected style and terminology, so it is worth specifying even in short prompts.
Educational illustration. Good prompt: 'Document the add(a, b) function on Python. Describe parameters as numbers, return — sum. Add an example add(2, 3) = 5. Note that passing strings performs concatenation. Limitation: no more than 100 words'.
Checking the result
After generation the text appears in a separate panel. It can be copied or downloaded in TXT format. Check whether all parameters are described, whether the return value matches the code, whether the example is correct. If the tool made an error, clarify the prompt and generate again.
The tool does not execute code, so examples may be syntactically correct but not verified on real data. If the function is complex, examples should be tested manually. This is especially important when documentation is intended for other developers.
The model may make errors, and queue and service availability may change. Always review the generated text before use. If the result is unsatisfactory, change the prompt formulation and add missing details.
Educational illustration. Check: the description should state that add returns the sum of a and b. If it says 'returns the difference', the tool erred — clarify the prompt.
Limitations and typical errors
The tool does not upload documents, does not perform batch processing, does not save history and does not integrate with other services. It does not create images, audio or video. Input is limited: 8000 characters for code and 1000 for platform. Output is plain text only. Export is possible in TXT; DOCX and PDF formats are not supported.
A typical error — too short a prompt without indicating language and purpose. Another error — overloading with irrelevant details. A third — expecting the tool to understand context if it is not described.
Do not ask the tool to do what it cannot do: for example, generate a DOCX or PDF. If another format is needed, the text must be converted manually. Also do not expect batch processing: the tool works with one fragment at a time.
Educational illustration. Error: 'Document all functions in the project'. The tool processes one fragment at a time. Better to split the task into several requests.
An accurate prompt is the foundation of quality documentation. Specify context, purpose, format and limitations, provide examples, check the result and if necessary refine the input. Remember the limitations: the tool does not execute code, does not save data and does not support complex formats. With a careful approach 'Code documentation writer' helps describe functions faster and keep documentation up to date.
Open tool: Code documentation writer ↗Interface translations are machine-assisted and may need refinement.