My documentation was blank when I tried to document some API endpoints I had just written a few months ago. The code worked fine, but I found the documentation largely lacking. It’s no secret that juggling deadlines in a software team can make documentation the last thing on your mind. This is when a colleague suggested an AI-driven tool that automatically generates documentation from your code. To be honest, I was skeptical. If something sounds too good to be true, it probably is, doesn’t it?
Well, after giving it a shot, I realized it wasn’t just some snake oil. Large language models (LLMs) can actually read through your repository and produce documents that are surprisingly accurate. The text still needed to be fine-tuned to better match our style and a few custom details filled in, but the amount of time I saved was impressive. My goal is to share what I’ve learned, so here are some notes on why AI-based documentation might be worth checking out, along with some tips and caveats.
The Real Pain of Manual Docs
If you’re anything like me, you’ve probably updated an API parameter or renamed a function, then forgotten to reflect it in your documentation. A week later, someone on your team tries to use that endpoint, only to find that the instructions have been outdated. You can keep frantic Slack messages to a minimum. Proper, accurate documentation eliminates entire categories of miscommunication. The better your documentation is, the smoother your workflow will be.
What LLMs Actually Do
As people talk about artificial intelligence and “code understanding,” it’s easy to think that the AI has magical, all-seeing abilities. However, these models are essentially pattern recognition tools. Using text analysis, they identify what documents should be included in texts by comparing those texts with known structures.
As long as the function says “getUserDetails,” the AI will infer it returns user-related data-often with helpful descriptions. The model isn’t perfect, however; there are times when it misses specialized logic or misinterprets complex snippets. The benefit of this method overwriting everything from scratch is that it’s a great starting point.
Big Myth: “AI Docs Will Be Riddled With Errors”
There were a few what-ifs in my first test run with an AI doc generator. (It claimed that one function took a parameter that hadn’t existed in months.) But for the most part, it worked well. I just need to make sure I go over it again properly.
This is similar to having a junior developer write the first draft of your documentation; it still needs revision and confirmation, but you’re not starting from scratch. When I write from scratch, it takes me more time to polish AI-written documents than I do when I write them from scratch.
Getting Past the AI Skepticism
Many developers are unconvinced, afraid they’ll have to fix more mistakes than if they had done it themselves. But consider this: how many times have you meticulously written documentation, only to have it languish and go stale within a month? Whenever you commit a major update, you can run AI-based documentation more frequently, ensuring your documentation stays current without having to spend a great deal of time and energy on it.
Integrating into Your Existing Workflow
In large teams using GitLab or GitHub, you can often find AI tools that integrate right into your continuous integration and continuous delivery pipeline, automatically updating the relevant documentation whenever significant code changes are made. The fact that your docs stay up to date with your code is very satisfying, almost like having a dedicated bot writer on hand.
How Much Does It Cost, Really?
It is true that some AI platforms charge monthly fees, and if you’re doing a side project on your own, that might seem steep. However, there are more and more open-source solutions showing up over the next few years. Moreover, if you consider how much time you’ll save (and how many headaches you can avoid), you might find it worthwhile to invest in a robust tool; even if you opt for open-source, you will likely find a decent starting point that will allow you to generate functional drafts with little hassle.
Practical Tips for Happier Automatic Docs
Keep your code base clean: It’s important to name your functions, variables, and docstrings in a descriptive manner, since models read them like breadcrumbs.
Curate a style guide: Your team can provide input on how AI-generated text should be worded, formatted, and examples in the final pass. Over time, you will refine a style that feels more “human” and consistent.
Check specialized cases: It will get lost in the weeds when it comes to domain-specific logic (like cryptography or advanced machine learning algorithms). When your expertise fills the gap, AI will be able to be a useful tool.
Conclusion
When it comes to writing extensive documentation for big, ever-evolving endpoints, I used to dread it. But LLM-powered tools have taken a lot of the stress out of it for me. If you start with a good foundation, you can quickly verify accuracy, add new context, and publish, resulting in current, reliable documents without spending a lot of time updating them manually.
It’s important to note that no AI solution is plug-and-play perfect. You still need to be cautious and ensure the solution is the right one. While AI-driven documentation isn’t a total replacement, it can significantly reduce the gap between your code’s state and your documentation if you approach it as a helper, rather than as a replacement. I’m also all for anything that helps me spend more time coding rather than browbeating myself for forgetting to update another docstring.
Frequently asked questions
What is LLM automation?
The LLM is a Deep Learning technology and a type of Artificial Intelligence specifically focused on understanding language, such as meaning, intent, or even translation. A well-known LLM model is the GPT, which stands for Generative Pre-trained Transformer.
Why is API automation better than UI automation?
Faster and More Efficient: Tests based on APIs are lightweight and execute much faster than UI tests, so engineers can carry out frequent tests and feedback loops much faster. Reduced Maintenance: APIs require much less maintenance, so engineers can focus on more strategic tasks.
How to use AI in API automation?
The use of Machine Learning (ML) and natural language processing techniques enables artificial intelligence to analyze vast data sets and API specifications and automatically generate comprehensive and relevant test cases. Through this process, the testing process can be accelerated and its effectiveness increased by identifying unexplored quality areas.
Why API test automation?
Tests based on API automation are more accurate and can be run more frequently with better accuracy than manual tests. API automation tests help developers detect issues much sooner, allowing them to fix them quickly. As a result, repairing them later, when they are more complicated, will be less expensive and time-consuming.