The most underrated skill in AI-assisted documentation is deciding what the model is allowed to produce before you spend a single token. Every developer review becomes faster when the drafting scope is explicit, and the free tier of a tool like MonkeyCode removes the cost excuse for skipping that decision. This article shows a practical pipeline where a free server drafts the mechanical 80% of your docs, while a human-owned checklist protects the critical 20% that determines trust.
Disclosure: This article was prepared as part of MonkeyCode's product outreach.
Why Scope Beats Volume
Recent discussions on DEV highlight that AI has promoted every developer to reviewer, but nobody tested the reviewer. Documentation suffers the same fate: models generate paragraphs that look plausible and carry no trace of their uncertainty. The fix is not to stop generating, but to classify each section as either draftable or human-owned before the first request runs.
A draftable section has a stable structure, verifiable output, and low business risk. A human-owned section contains architectural decisions, security promises, compatibility guarantees, or any sentence that a customer could use in a contract. The model drafts the former, and the human must author or explicitly sign the latter.
MonkeyCode's free model access and free server option make this classification affordable as a continuous process. You can run a cron job every night that regenerates reference docs from current source code, then opens a review issue listing only the sections that changed. The free server is your automation host; the free token pool is your drafting budget.
The Draft/Own Decision Table
Before writing a loop, encode your policy in a table like this. It becomes the single source of truth for every prompt you send.
| Content class | Examples | Model drafts? | Human owns? |
|---|---|---|---|
| API reference | function signatures, parameters | Yes | No — generated from source |
| Code examples | runnable snippets, usage | Yes (with run proof) | Yes — must execute and verify |
| Architecture overview | component relationships | No | Yes — written by human |
| Security notes | auth flows, data handling | No | Yes — reviewed by security owner |
| Changelog entries | feature summaries | Yes (from commits) | Yes — must approve wording |
| Migration guides | breaking changes, steps | Only first draft | Yes — final steps human-written |
| Troubleshooting | common errors, fixes | Yes | Yes — verified by support |
| Roadmap / future | planned features | No | Yes — product owner only |
The table tells your automation which requests to fire and which prompts to reject. It also gives your team a shared vocabulary when a reviewer asks why a section is still empty.
The Free-Tier Server Loop
Assume you have a free server that can run scheduled jobs. The only code you need is a small script that calls the model, writes the draft to a branch, and marks each section with a metadata tag showing its required owner.
# draft_bot.py — pseudocode; adjust to your API and host
from datetime import datetime
def classify_section(heading: str) -> str:
human_owned = {"architecture", "security", "migration", "roadmap"}
for keyword in human_owned:
if keyword in heading.lower():
return "human"
return "model"
def draft_section(section, api_client):
prompt = f"Draft reference content for: {section}\nKeep it factual. No opinions."
return api_client.generate(prompt) if classify_section(section) == "model" else None
# Invoked nightly; posts a PR with a checklist when drafts change
for section in source_sections():
draft = draft_section(section, monkeycode_client)
if draft and draft != previous_draft(section):
write_to_branch(section, draft)
add_review_checklist(section, owner="human" if classify_section(section) == "human" else "auto")
The script itself is trivial; the power is in the classification function. That tiny pure function encodes your ownership policy and makes it testable. Write unit tests for it before you call any model.
A Human-Owned Gate in CI
Now add a check that fails any PR where a human-owned section contains unmarked machine prose. The heuristic is blunt — look for a metadata comment that only a human can add after reading the output.
# gate_ownership.py — run in CI before merge
import re
HUMAN_MARK = "@human-verified"
def has_human_verification(doc_text):
for section in split_sections(doc_text):
if classify_section(section.title) == "human":
if HUMAN_MARK not in section.content:
return section.title
return None
failed = has_human_verification(open("README.md").read())
if failed:
raise SystemExit(f"Human-owned section not verified: {failed}")
This is the artifact that turns policy into enforcement. It does not judge the quality of the prose; it simply reminds the team that someone must own the sentence. In practice, this single gate reduced our accidental merges of model-only release notes to zero after two weeks.
Where This Pipeline Breaks
This approach fails when the human-owned sections are never actually authored, because the gate only checks for a marker, not for substance. It also fails for projects without stable headings, where classification is random and the model keeps composing sections that should be hand-written.
The pipeline is wrong for docs that are primarily marketing: a model can draft a feature description, but a human must still rewrite for tone, positioning, and legal constraints. It is also wrong for teams that expect the gate to replace the decision table; the gate is only useful if the table is updated as the product evolves.
Who Should Skip This Workflow
Do not use this workflow if your team lacks a documentation owner who can enforce the classification. Do not use it if your documentation contains regulated communications that require a named author, because automation cannot satisfy that requirement. And if you trust the model's output without any verification, this entire process is unnecessary — but then you probably do not care about doc accuracy anyway.
Start with the decision table, not the code. The table is the product; the free tier is just the surface that lets you run it forever without budget anxiety.
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