By Mark Table of Contents
By the time a project reaches Project 1 – Step 5, the code works and the tests pass, but one job is left: documentation. Most engineers dread it, yet it is what keeps software maintainable. I haven’t automated this in a DevOps pipeline yet, but even ad-hoc use has changed how I write docs, so here is what I’ve seen.
One of the most useful things AI does for a development team isn’t code generation, it’s documentation. It has moved past simple comment generation and can now draft a large part of a project’s knowledge base. This post covers why AI is good at this, roughly how much time it can save, and where a human still needs to step in.
Why AI Is Good at Documentation
Why is AI Documentation catching on? Largely because LLM context windows have grown a lot. Models can now take in much of a repository at once, so they can describe more than a single file and infer something about the design of the whole system.
Consistency Across the Codebase
When humans write documentation, the quality is often variable. It fluctuates based on the developer’s energy levels, time constraints, or impending deadlines. Module A might be documented with academic rigor, while Module B is left with vague descriptions.
AI doesn’t get tired, so it can keep a fairly uniform “Tone & Manner” across a large codebase, whether it’s naming conventions, architecture explanations, or inline comments. Consistent doesn’t mean correct, though, so the output still needs a human review.
Reverse Engineering the Intent
Developers write code with a mental model of the logic, but translating that abstract logic into prose is a separate skill set—one that causes significant friction. AI is good at working backwards from finished code to explain not only “what” it does but a plausible “why”. That “why” is a guess, so check it against the real intent.
There is also the problem of “Stale Documentation”: docs rot as code evolves. Some tools now detect code changes and update the docs, which can reduce that drift.
Efficiency: Writing by Hand vs. an AI Draft
“Faster” is vague, so here are rough numbers. They are assumptions to illustrate the idea, not measurements.
Time Cost (Estimate)
Consider the standard workflow for creating API specifications (like OpenAPI/Swagger) or architectural whitepapers for a medium-sized project. Let’s assume a human engineer needs about 3 to 4 days (roughly 24–32 hours) to draft, refine, and format these documents.
With an AI draft and a human review, the same work might look like this (also assumed):
- Draft Generation: ~10 minutes (assumed)
- Human Review & Refinement: ~2 hours (assumed)
- Total Time: ~2 hours and 10 minutes
If those assumptions held, that would be a large saving. In practice it depends on project size and how good the AI draft is. The developer moves from a “writer” staring at a blank page to an “editor” who validates technical accuracy, which leaves more time for design decisions.
Less Cognitive Load
The hidden cost of documentation is Context Switching. Shifting from “coding mode” (logic, syntax, abstraction) to “writing mode” (explanation, empathy, structure) takes a lot of mental energy. If AI drafts the text, developers can skip much of that switch and concentrate on checking technical accuracy.
Nuance and Business Logic
Historically, critics argued that machine-generated text was robotic and lacked the “human touch.” That gap has narrowed a lot.
Contextual Understanding and Storytelling
Early AI models merely described function inputs and outputs. Current models can add context. For example, when documenting a process_payment() function, the AI may explain the business role instead of only listing parameters (results vary by model and prompt): “This module orchestrates communication with the payment gateway to ensure transaction atomicity, preventing double-billing scenarios.”
RAG (Retrieval-Augmented Generation)
By utilizing RAG, organizations can train AI on their internal wikis, legacy documents, and specific coding guidelines. The output can then pick up the company’s jargon and acronyms. It is often hard to tell a junior developer’s writing from an AI’s, but polished-looking docs can still be wrong, so accuracy needs its own check.
Beyond the Code: The Expanding Scope of Documentation
Many people think of documentation as code comments or API references. AI also helps with documentation beyond the code.
1. Onboarding Guides and Troubleshooting Manuals
For new hires, understanding a legacy codebase is a daunting task. AI can summarize a repository into an “Onboarding Handbook” covering the core architecture and data flow. It can also go through past issues and commit logs to draft a “Troubleshooting Guide” of common errors and fixes.
2. Functional Specifications for Non-Technical Stakeholders
Product managers and marketers cannot read code, yet they need to understand feature implementation. AI can translate technical implementations into business language and draft functional specifications, which helps cut communication overhead between departments.
3. Changelogs and Release Notes
Git commit messages are notoriously brief or vague (e.g., “fix bug”). AI can read the diff and write user-friendly release notes, turning a cryptic commit into a clear statement, for example: “Fixed a critical crash in the checkout flow that occurred when the ‘Back’ button was pressed during payment processing.”
Conclusion: AI Drafts, Humans Review
Wrapping up Step 5 of Project 1, my takeaway is that I don’t want to keep writing documentation entirely by hand. I haven’t put AI Documentation into a CI/CD pipeline yet, but I’d like documentation to become a normal step there.
It doesn’t replace the developer — it takes the drudgery out of documentation so I can spend more time on the architectural decisions that actually need a human. The gap between human- and AI-written docs has narrowed a lot. Since technical docs care more about accuracy and staying current than about “emotional” writing, I’ve come to prefer the AI-assisted workflow, as long as a human checks the accuracy.
If AI Documentation isn’t part of your workflow yet, it’s worth trying — for me the drafting is now mostly AI, and I focus on reviewing and refining what it produces.