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The short answer
The workflow splits the job into two parts. Git provides the facts: the latest tag, its date, and every commit between that tag and HEAD. A human decides the next version. An optional language model only rewrites commit subjects into calm, customer-facing sentences, one for each commit, in the same order. A final script assembles a draft file. Nothing is published automatically.
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The approach comes from a tutorial by Sam Rivera, published on DEV Community with a September 16 posting date that does not show a year. The example is a Python script set for a solo maintainer of a small CLI who wants a paragraph for a changelog within about an hour.
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A model asked to write a changelog will often also be asked, implicitly or explicitly, what the next release is called. That question has a correct answer only when a person knows what changed for users, whether a change breaks compatibility, and what the project’s versioning policy requires. The author’s position is direct: “Semver is a human decision.”
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Keeping the next version out of the model’s input removes the temptation to let it guess. The facts file the scripts produce contains a field named next_tag and sets it to null. The model never sees a candidate version, so it has nothing to echo back. The author’s framing of the model’s role is equally narrow: “The model, when it answers, is only a copy editor.”
The three stages
Each stage has a defined input and output, and only one of them is allowed to introduce text that was not already in the repository.
| Stage | Reads | Writes | Allowed to change the version? |
|---|---|---|---|
| 1. Extract facts locally | Git history: latest tag, tag date, commits after the tag | A facts file with next_tag: null |
No. The next tag is deliberately left unset. |
| 2. Rewrite wording (optional) | Commit subjects only | One edited sentence per subject, or the local echo fallback | No. The requested output contains no tag, date, or SHA. |
| 3. Stitch the draft | The facts file and the edited sentences | out/changelog.draft.md |
No. The existing tag is printed as the baseline, and version-like numbers in sentences are replaced. |
Stage 1: Extract facts locally
The first script runs Git commands to find the most recent tag, the date of that tag, and the commits between that tag and HEAD. It writes them to a facts file that holds:
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- the current tag and its date;
- the commit count;
- each commit identifier and its subject;
- a breaking-change flag that a human supplies;
next_tag: null.
Because the breaking-change flag is entered by a person, the release decision that depends on it stays with that person rather than with the model or the script.
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Stage 2: Rewrite wording (optional)
The second pass sends the commit subjects to an optional endpoint with instructions to return one calm, customer-facing sentence for each subject. The request asks for wording only. The returned text should not include a tag, a date, or a commit SHA.
The script falls back to local echo, which means the original subjects pass through unchanged, in three cases:
- no endpoint is configured;
- the call fails;
- the number of returned sentences does not match the number of commits.
The count check matters because the stitcher pairs sentences with commits by position. A missing or extra sentence would shift every entry after it.
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The final script builds out/changelog.draft.md. It prints the existing tag as the baseline, then pairs each original commit identifier with its edited sentence. The identifiers and baseline come from the facts file, not from the model’s output.
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There is one guard on the text itself. If an edited sentence contains a version-like number, the script substitutes the original commit subject. The author presents this as a guard in the example. It is not proof that every invented claim or identifier will be caught. A sentence can describe a change inaccurately without containing a number at all, so the human review step in the next section is still required.
Keeping publication under human control
The example never writes to CHANGELOG.md, never pushes a tag, and never opens a release. The maintainer reads out/changelog.draft.md and copies the text into the project changelog if it is acceptable.
Rollback is equally simple. Deleting the out/ directory removes everything the workflow generated, and the project’s history and changelog remain untouched.
Handling commit subjects you should not copy
The local fallback and the optional endpoint both work from commit subjects, so the workflow copies subjects into the facts file and, when the endpoint is configured, sends them to that service. If a subject contains customer data, a personal name you would not publish, or a secret, that text should not enter the facts file or leave your machine. The author lists this as a reason to skip the approach entirely. Check subjects before you run the first script, especially on a repository that has accepted contributions from people outside your organization.
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When this approach is the wrong choice
The author names several situations where the workflow is not the right fit:
- the project already has a release train with its own changelog process;
- signed tags are used as a legal artifact, so the wording of the tag or changelog has formal consequences;
- a customer-facing changelog is parsed by a schema, and free-form sentences would break it;
- the project already uses release-please or semantic-release, which handle versioning and notes;
- writing the entries by hand is faster for the maintainer than setting up the scripts;
- commit subjects are sensitive and should not be copied into a facts file.
The last two points are the ones most likely to matter for a small project. If you already have a tool that derives versions from commit conventions, adding a model step adds moving parts without adding much.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence supports
This is a single tutorial that describes example code and intended behavior. It does not report a controlled test, production use, or measured reliability, and the author does not claim the method eliminates hallucinated content. The numbers in the example are illustrative: 0.3.2 and 0.4.0 are fixture version strings, three commits is the size of the sample history, and sixty minutes and zero dollars frame the author’s own scenario. None of them is a measured result, and none is attributed to a named study.
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A note on the optional editor service
The article mentions that a remote editor or server can be used for the optional wording step and describes it as free. That is the author’s claim and was not checked against the provider’s current terms. The scripts’ local fallback means the workflow runs without it, so you do not need to adopt that service to follow this method.
This article was prepared as part of MonkeyCode’s product outreach.
Source
Sam Rivera, “Keep the Next Tag Out of the Prompt,” DEV Community. The posting date is shown as September 16 with no year, and the page was accessed on 2026-10-07.
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