The Case for Comment-Driven Development

Comment-driven development flips the script on AI-generated comments: instead of discarding them as noise, engineers treat them as thinking prompts that must be vetted and refined. By actively curating the "why" behind code, teams turn vague, over-commented output into precise, reusable context that guides both human reviewers and future AI assistants.

LLMs habitually produce verbose, sometimes inaccurate comments that describe what the code does rather than why it exists. The article highlights a concrete failure where a generated comment claimed a function checks "over limit" while the code actually used "greater than or equal to," causing both humans and subsequent AI agents to misinterpret behavior. Over time these mismatches accumulate as technical debt, with stale comments drifting away from the code they describe.

The proposed workflow makes comments a first-class artifact of code review. Engineers keep descriptive function names, write focused docstrings that capture contracts, and add cross-reference notes when logic is duplicated. A two-layer documentation model-comments for the "why" and unit tests for the "what"-ensures AI agents have the business context they can't infer from code alone while tests provide verifiable specifications.

Tactical advice includes preferring explicit names over explanatory comments, writing method-level docstrings that improve language-server autocomplete, inserting explicit cross-reference comments for duplicated logic, and instituting a comment quality gate in pull-requests that forces reviewers to remove redundant commentary and surface essential context before merging.

When teams embrace this disciplined approach, the overhead of AI-assisted coding drops dramatically. High-signal comments accelerate code comprehension, reduce mis-alignment between intent and implementation, and ultimately let engineers ship three times faster than peers who cling to the myth of self-documenting code alone.