An AI-generated API description can sound authoritative and still document an endpoint that is not in the code. In an article published on DEV Community on September 19, 2026, Babar Khan describes Docloom as an attempt to reduce that risk by dividing the work: a parser extracts API facts from a repository, then an AI model turns those facts into prose. Khan sums up the idea this way: “The AI describes. It never discovers.”
The problem: a convincing description of an endpoint that did not exist
Khan’s account begins with an AI-generated documentation draft that confidently described an endpoint absent from the project’s code. The example is the author’s anecdote, not a measured estimate of how often AI documentation is wrong. Its significance is the mismatch between fluent writing and verified API behavior: polished prose can make an unsupported claim look settled.
The proposed response is to stop asking the writing model to decide what the API contains. Instead, the article assigns API discovery to a parser and reserves explanation for the model.
How the parser-and-model workflow is supposed to work
- Extract facts from the repository. A parser examines code and produces facts about the API.
- Write from those facts. The language model uses the parser’s output to explain the API rather than independently inventing or discovering its contents.
- Review the proposed documentation changes. The article says changes are presented as a diff after a merge.
- Approve before publication. A developer must review and approve the diff before the documentation goes live, according to Khan’s description.
Khan compares the model to “a writer who’s only allowed to write about facts a fact-checker already signed off on.” The analogy captures the intended boundary: the parser supplies the factual input; the model supplies the readable explanation.
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What this approach may improve—and what it cannot establish
| Question | LLM discovers and describes | Parser supplies facts; LLM describes |
|---|---|---|
| Where API claims originate | The model’s interpretation of the code | Structured facts extracted from the repository, as described in the article |
| How proposed changes are reviewed | No review process is specified in the article for this general approach | The article says a developer reviews a diff and approves it before publication |
| What a human still needs to check | Whether the model’s claims match the actual API | Whether parser-derived facts are complete and correct, and whether the resulting prose accurately explains them |
Separating extraction from prose generation could make unsupported endpoint claims less likely, but the article does not describe the parser’s validation method in enough detail to establish formal guarantees. A parser can only ground the model in what it extracts; the account does not show that extraction is complete or error-free, or that the model cannot misstate facts it receives. The described approval step leaves a person responsible for checking the result.
What the article says about Docloom
Khan’s article presents Docloom as the tool built around this workflow. It reports that the service was free to try without a credit card and that Khan wanted sample repositories and feedback to learn where it failed across different technology stacks. Those are claims in the September 19, 2026 article, not confirmation of present-day availability or terms.
Current product status, supported languages and frameworks, integrations, repository permissions, security practices, data retention, and commercial terms are not established by the available account. It also does not provide an independent benchmark or compare Docloom with competing tools.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who should find the idea useful
The design is relevant to teams considering AI-assisted API documentation, especially those concerned that a model might fill gaps in its understanding with plausible but unsupported details. The practical lesson is to distinguish evidence gathering from explanation, and to make proposed changes reviewable before publication. The article offers a design rationale and one motivating example—not proof that this workflow prevents errors or improves documentation by a measured amount.
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