Writing documentation is easily the most tedious part of our software development lifecycle. I want to see how engineering teams are deploying ChatGPT at work to generate inline comments, API documentation, and changelogs. Does it handle complex legacy architectures well, or does it require too much manual correction to be useful?
3 answers
Our development team uses an IDE extension powered by LLMs to document legacy codebases. It is incredibly efficient at interpreting poorly commented Python and C++ scripts and turning them into clean Markdown files. The trick is to provide the tool with explicit style guides and architecture schemas beforehand. Without that guardrail, the generated documentation tends to be overly verbose and misses internal library dependencies.
Does your team run automated validation checks on the AI-generated comments to ensure they actually match the logic of the updated codebase during CI/CD pipelines?
It has reduced our documentation debt by nearly half, especially when creating standardized README files for newly initialized repositories across microservices.
Reducing documentation debt by half is massive. Deploying ChatGPT at work for boilerplate files allows engineers to maintain high development velocity without sacrificing project maintainability.
Yes, we implemented a peer-review gate where developers must verify the accuracy of the documentation before merging the pull request. We never let the automated output bypass human eyes, especially when updating critical API endpoints for external clients.