Across the UK’s technology and research sectors, poorly written API documentation continues to cost organisations time, money, and credibility. Whether in universities, public-sector digital services, or private tech firms, unclear documentation harms adoption, complicates onboarding, and contributes to software failures. As someone who serves on a UK academic committee regularly reviewing digital-research outputs, I see the consequences of incomplete, inconsistent, or opaque documentation nearly every month.
Yet we now stand at a turning point. Large language models—particularly ChatGPT—offer a practical, accessible, and surprisingly powerful tool for generating high-quality API documentation. Not documentation that merely looks polished, but documentation that is structured, accurate, user-centred, and maintainable.
This article offers a full-length, pragmatic, research-informed guide aimed at the British general reader: developers, students, policymakers, digital-skills educators, and anyone who works alongside modern software systems. My goal is not to promote technological hype but to offer evidence-based advice and clear steps for using ChatGPT responsibly and effectively.
API documentation is often treated as an afterthought—a chore, an optional extra, or a task assigned to the most junior member of a development team. Yet, in practice, it is the interface between your software and the rest of the world. Bad documentation means:
Higher support costs
Lower adoption rates
Frustrated developers
Increased security risks
Avoidable project delays
Inconsistent implementations
In the UK’s public sector—where APIs power everything from tax systems to NHS digital tools—documentation quality can directly affect service delivery, accessibility, and trust.
Yet documentation remains chronically underfunded and undervalued. Many organisations try to “fix” bad docs through long meetings, expensive consultants, or the latest documentation framework. Few consider a simpler option: using ChatGPT as a first-pass generator, an editor, or even a collaborative writing partner.
ChatGPT is not simply a text generator; it is an exceptionally powerful pattern recogniser. Most API documentation follows clear conventions:
Endpoints
Methods
Parameters
Error codes
Example requests
Example responses
Authentication details
Versioning notes
These are exactly the kinds of structured patterns large language models excel at producing. With proper prompts, ChatGPT can:
Read existing code and describe it in plain English
Generate consistent endpoint reference tables
Produce examples in multiple programming languages
Suggest improvements to naming conventions
Rewrite documentation for specific audiences
Convert informal notes into formal documentation
Maintain consistent formatting across hundreds of pages
In many cases, ChatGPT produces documentation that is clearer than the original code itself.
It is important, especially in a UK policy context, to emphasise that ChatGPT should not replace developers, technical writers, or domain experts. Instead, it serves as an accelerant—a tool that can transform rough drafts into polished explanations or turn code into structured documentation more rapidly than traditional manual writing.
Human oversight remains essential for:
Verifying accuracy
Ensuring security-critical details are correct
Maintaining compliance with British standards
Reflecting organisational policy
Avoiding hallucinated behaviour
Used responsibly, ChatGPT reduces human workload while improving output quality. Used irresponsibly, it can propagate errors at scale. The advice that follows addresses both opportunities and risks.
Below is a complete, evidence-based process suitable for UK teams, universities, and individual developers.
The primary rule: never let ChatGPT guess. The model is most reliable when analysing real code. Provide:
Function definitions
Class declarations
OpenAPI/Swagger schemas
GraphQL schema files
JSON examples
Typescript interfaces
Curl examples
If confidentiality is a concern, provide representative, sanitised samples or use tools like ChatGPT with enterprise-grade security (used widely in UK universities and companies).
A strong “role prompt” sets expectations. For example:
“You are a senior technical writer preparing formal API documentation for UK developers. Follow a clear, consistent structure. Write in concise, neutral, British English.”
You may also supply your organisation’s writing guidelines or ask ChatGPT to analyse an existing documentation style.
Ask ChatGPT to generate structured documentation such as:
Endpoint overview
Parameter tables
Example requests/responses
Authentication notes
Pagination rules
Error-handling details
Rate-limiting policy
Version-ing conventions
Code snippets in multiple languages
Consistency is key. You can enforce a template:
“Use the following sections: Description, Method, URL, Parameters, Authentication, Example Request, Example Response, Errors.”
ChatGPT excels when used interactively. After the first output, try:
“Rewrite this section for beginners.”
“Add Node.js and Python examples.”
“Ensure parameter descriptions are precise and unambiguous.”
“Expand the error section with HTTP status codes.”
“Rewrite using GOV.UK style guidance.”
Iterative prompting yields far stronger documentation than a single output.
One under-used feature is ChatGPT’s ability to audit its own work:
“Check for missing parameters.”
“Verify that the examples match the schema.”
“List any ambiguities.”
“Identify potential security risks.”
This acts as a sanity check before human review.
You can generate multiple versions:
Beginner-friendly
Expert-level
Business audience
Educational version for UK schools/universities
Accessibility-focused version following WCAG
ChatGPT makes multilingual versions trivial—useful for projects serving global users.
ChatGPT can generate:
API changelogs
Migration guides
Tutorials
Quick-start guides
Troubleshooting pages
Architecture diagrams (described in text)
Release notes
This elevates your documentation from adequate to outstanding.
Imagine you provide ChatGPT with a Python function:
def create_user(name: str, email: str, is_admin: bool = False): if not email.endswith("@university.ac.uk"): raise ValueError("Invalid email domain")
user_id = save_to_database(name, email, is_admin) return {"id": user_id, "name": name, "email": email, "admin": is_admin}
A strong prompt would be:
“Write API documentation for the function below. Include a clear description, parameter table, example request/response, errors, and notes for UK institutional use.”
ChatGPT will then transform this into:
A human-readable endpoint description
A parameter table with types and constraints
Example JSON payloads
Error handling documentation
Domain-specific warnings
This is dramatically faster than writing such documentation manually.
From university research software to government APIs, ChatGPT offers practical gains:
Document research code for reproducibility
Support digital-skills curricula
Generate teaching materials
Improve accessibility in digital projects
Standardise documentation across departments
Improve developer experience for public APIs
Reduce onboarding times for contractors
Produce clearer public-facing guidance
Accelerate release cycles
Reduce support tickets
Improve integration experiences
Enhance security by reducing undocumented behaviour
As a UK academic adviser, I must emphasise that ChatGPT documentation must always be verified by humans. Risks include:
Misinterpreting ambiguous code
Inventing behaviour that does not exist
Missing security considerations
Overlooking deprecated features
Writing examples that do not run
Five mitigation strategies:
Provide real code rather than descriptions
Use iterative refinement
Ask ChatGPT to list uncertainties
Require human verification
Use models designed for accuracy (e.g., ChatGPT 5.x series)
Used responsibly, the benefits far outweigh the risks.
Britain has long championed rigorous technical standards and world-leading digital governance. The rapid adoption of AI-assisted documentation could:
Reduce development inefficiencies
Improve cross-institutional collaboration
Strengthen public-sector digital services
Support the next generation of UK developers
Enhance transparency and trust
Lower long-term costs
In five years, it is likely that AI-generated documentation will become the norm rather than the exception. The UK has an opportunity to lead—not by replacing humans, but by equipping them with better tools.
Here is a reusable prompt for any UK reader:
PROMPT TEMPLATE
“You are a senior technical writer producing formal API documentation in clear British English. Use the following structure: Overview, Endpoint, Method, URL, Parameters, Authentication, Example Request, Example Response, Error Codes, Notes, Versioning. Analyse the following code/schema precisely. Do not invent features. Ask clarifying questions if needed.”
This template alone can improve documentation quality dramatically.
The biggest misconception in the UK public debate is that AI tools like ChatGPT remove the need for technical writers or documentation teams. In truth, they make these roles more essential. Human expertise ensures accuracy, contextual understanding, and alignment with organisational goals, while ChatGPT provides speed, consistency, and flexibility.
Together, they can produce documentation that is clearer, more inclusive, and more reliable than either could achieve alone.
The future of British software development hinges on our ability to communicate clearly—not just to machines, but to one another. ChatGPT, used wisely, can help us do exactly that.