Every programmer has at some point inherited undocumented code that nobody understands, not even whoever wrote it. In 2026, AI is a great ally for understanding and documenting legacy code… if you use it with judgement. Here’s how, with what genuinely works and the warnings worth keeping in mind.
Why AI helps so much here
Reading someone else’s code and reconstructing what it does is slow and frustrating. AI can explain a function, summarise a whole module or translate convoluted logic into plain language in seconds. To get context fast before touching anything, it’s about as useful as it gets.
How to use it to understand code
- Ask for explanations, not just summaries: “explain what this function does and why” gives you more than a generic summary.
- Go from general to specific: first the module’s purpose, then the hard parts.
- Ask about risks: “what could break if I change this?”. It saves you scares.
How to use it to document
- Generating comments and docstrings you then review and adjust.
- Creating a README or architecture guide from the code.
- Summarising what each part does for whoever comes next.
The important warning
AI can misread complex or tricky code and confidently describe something that isn’t true. Verify its explanation against the real behaviour before trusting it, especially if you’re documenting it for others. Wrong documentation is worse than none.
Our experience with legacy code and AI
- What saves us most: getting context on an unknown module in minutes instead of hours. That alone is worth it.
- The mistake we made: documenting exactly what the AI explained, without cross-checking. In a couple of cases it misdescribed tricky logic. Now we verify before writing anything final.
- The trick that works best: asking it to add input and output examples. Checking those examples against the real code confirms (or debunks) its explanation.
Our advice: use AI to understand and kick off documentation fast, but validate its version against how the code actually behaves. It massively speeds up the most thankless work, without giving up rigour.
The prompt that stops AI hallucinating about your code
The biggest risk when documenting legacy code with AI is that it invents what it doesn’t understand. The trick is forcing it to stick to what it sees: “document ONLY what you can infer from this code. If something is unclear or depends on external context, mark it as ‘needs verification’ instead of assuming it.” That separates what the AI knows from what it guesses. And always, for critical functions, check its explanation by running the code or asking whoever wrote it. AI is a great translator from code to human language, but one that sometimes fills gaps with invention: your job is to catch those gaps.
Making sense of legacy code with AI
Inheriting a tangled, undocumented codebase is where AI genuinely earns its keep:
- Explain, then summarise: paste a function and ask what it does, its inputs/outputs and side effects — a fast map of unfamiliar territory.
- Generate the missing docs: have it draft comments and a high-level overview you then correct.
- Trace dependencies: ask what calls what before you change anything.
The caveat: AI explains what the code appears to do, not what the business intended — it can confidently misread a subtle bug as a feature. Use it to orient fast, then verify the critical parts by reading and running them yourself before you touch production.
Frequently asked questions
Can I trust AI’s explanation of code I don’t know?
As a starting point, yes; as absolute truth, no. Verify with examples and the real behaviour, especially on complex logic.
Can AI document a whole project on its own?
It can generate a complete first draft, but it needs your review. The documentation that’s worth anything is the kind a human has verified.
Conclusion
Understanding and documenting legacy code stops being a nightmare with AI used well: ask for explanations, verify with examples and review before publishing. For the rest of the flow, see how to review AI-generated code and the terminal agents comparison.
