How to Review AI-Written Technical Articles Before Publishing

The review gate is not an AI detector. It is a publish-or-do-not-publish decision with evidence.

That distinction matters. A technical article can sound human and still contain a false benchmark, a fake citation, a missing disclosure, or a broken internal link. It can also be AI-assisted and still be useful when the draft is sourced, edited, and checked against a clear assignment.

Google's public guidance does not treat all AI assistance as a violation. It focuses on whether content is helpful, reliable, people-first, and not produced mainly to manipulate rankings through low-value scale. That puts the burden on the publisher: if an AI-assisted draft goes live, the site needs a review process that can explain why it was worth publishing.

This guide gives you that review process for technical articles. It is built for small content teams, developer-run sites, and agent-assisted publishing workflows where a draft moves through brief, research, writing, review, build, and deployment.

AI-assisted disclosure: this article was produced from a planner brief and source-backed research packet, then reviewed against KnowToAct editorial, source-quality, and publication-safety gates.

The goal is not to prove a human wrote it

Do not make the review depend on whether a detector says the article is AI-written. That is the wrong center of gravity.

The useful question is narrower:

Would we publish this article if we had to defend every claim, source, example, and production check?

For technical content, that means the reviewer should look for evidence:

• Does the article answer the promised reader problem?
• Are important claims tied to sources or direct project evidence?
• Are source limitations visible?
• Is there original value beyond a summary of public docs?
• Does the prose sound like a practitioner explaining a workflow, not a tool landing page?
• Would the build, sitemap, RSS feed, and production page expose only accepted content?

This is why a review gate should return an explicit verdict. Borrow the shape from code review: approve, request changes, or block. GitHub pull request reviews and status checks are not editorial standards, but they offer a useful operating model: a reviewer leaves a decision, and deterministic checks run beside human judgment.

The five checks every AI-assisted article needs

Keep these checks separate. When they get mixed together, reviewers miss problems.

Check	Main question	Common miss
Factual support	Can important claims be traced to sources, project evidence, or direct examples?	The draft makes confident claims because the model wrote them fluently.
Source quality	Are the sources appropriate for the claim?	Vendor docs are used as proof of neutral performance or business outcomes.
Original value	Does the article add a useful artifact or judgment?	The page summarizes public docs with no table, checklist, example, or decision rule.
Editorial and AI tone	Is the prose concrete, modest, and useful?	The draft uses inflated phrases, vague authority, and generic transitions.
Technical readiness	Will the correct page, metadata, links, sitemap, and RSS output be published?	The article reads well locally but the site build or production deploy is wrong.

A reviewer can approve one check and fail another. A draft with good sources but generic filler needs editing. A clear article with an unsupported benchmark claim should be blocked until the claim is sourced, narrowed, or removed.

Start with the assignment, not the draft

Review begins before you read the body text. Open the assignment first.

For an agent-assisted article, the assignment should usually include:

• the planner brief;
• the research packet or source notes;
• the intended reader;
• the target search intent;
• claims that need evidence;
• required original-value artifacts;
• internal links to use;
• publication constraints, such as no affiliate recommendations or no unattended deploy.

If there is no brief and no research packet, the draft is not ready for normal review. The first review comment should be simple: create the missing assignment artifacts or rewrite the article as an explicitly exploratory draft.

This matters because a fluent draft can redefine its own goal. A model may write a general overview when the brief asked for a practical release checklist. It may add a section about tools or monetization because those are common patterns on the web, even when the assignment forbids them.

Compare the draft to the assignment before polishing sentences.

Review scorecard

Use a scorecard that forces the reviewer to record both judgment and evidence.

Gate	What to inspect	Pass condition	Request changes when	Block when
Assignment fit	Brief, target reader, article scope, search intent	The article solves the promised problem for the intended reader.	The angle is useful but the order, examples, or scope drift need correction.	There is no clear reader outcome, or the draft answers a different assignment.
Factual support	Claims, citations, examples, numbers, tool behavior	Important claims map to reliable sources, project evidence, or direct examples.	A claim is plausible but needs a stronger source or narrower wording.	A claim is false, fabricated, unverifiable, or central to the article but unsupported.
Source quality	Source type, publication context, vendor bias, freshness	The source fits the claim type and limitations are visible.	A vendor source needs a caveat or a primary source should replace a weak reference.	Sources are fake, unreachable, padded, or used to support claims they do not address.
Original value	Tables, templates, examples, checklists, diagrams, decisions	The article gives the reader a reusable artifact or practical judgment.	The artifact exists but is too thin, generic, or disconnected from the article.	The article only restates public docs or competitor posts.
Editorial clarity	Opening, headings, transitions, examples, conclusion	The article is concrete, modest, and easy to act on.	There are generic sections, inflated claims, or unclear transitions.	The page reads like unedited generated filler.
Anti-AI tone	Vague authority, stock phrases, over-neat structure, buzzwords	The prose sounds like a practitioner explaining a real workflow.	A few passages need plainer rewrites.	The tone undermines trust across the article.
Technical publishing	Frontmatter, links, source count, build, generated index, sitemap/RSS, production body	CI and public-output checks pass for the intended stage.	Metadata, link, or generated-output issues are small and fixable.	Drafts leak, build fails, feeds are wrong, or production returns stale content.
Policy and monetization	Disclosures, affiliate/tool recommendations, legal/safety caveats	No hidden monetization or unsupported compliance claim appears.	A disclosure or caveat needs clearer wording.	The article includes monetized recommendations, legal claims, or risk claims without review.

For KnowToAct, a draft should not move from drafts/agents/ to content/agents/ until every blocking issue is resolved and the technical checks pass.

Decision table

The scorecard should lead to a decision. Do not hide the verdict in comments.

Finding	Decision	Reason
Important benchmark, traffic, revenue, or ranking claim has no source	Block	Technical readers may act on the claim; unsupported performance claims should not ship.
Source list exists, but most sources are vendor marketing pages	Request changes	The article may still work, but the source mix needs caveats or better primary sources.
Strong article, weak opening, generic conclusion	Request changes	The useful core exists; rewrite the weak sections before publication.
Article has facts and sources but no original artifact	Request changes or block	KnowToAct articles should give the reader a table, checklist, template, workflow, or decision rule.
Clear prose, but internal links point to missing pages	Request changes	Fixable technical issue. Do not publish until links resolve.
Local build passes, but production page returns a stale homepage	Block publication completion	HTTP 200 is not acceptance. Production must contain the expected title or slug.
Draft appears in sitemap or generated article index before approval	Block	The publication safety gate failed. Fix path/status filtering before reviewing prose.
Tool recommendation includes affiliate or sponsorship language without disclosure	Block	Monetized recommendations need explicit disclosure and a separate review gate.
The article claims a checklist guarantees Google compliance, accessibility conformance, or AI risk compliance	Block	A lightweight editorial process cannot promise compliance outcomes.

Risk frameworks such as NIST AI RMF and OWASP's LLM guidance can help a team name risks and controls. They do not turn a short article checklist into a compliance program, accessibility audit, or security review.

A block is not a judgment on the writer. It means the article should not be published in its current state.

Anti-AI-tone review without using AI detectors

AI-tone review is not about making every sentence sound quirky. It is about removing the parts that make the article feel ungrounded.

Look for these tells:

• vague authority: "experts agree" without naming the source;
• inflated claims: "this workflow transforms publishing";
• generic openings: "In the fast-changing world of AI...";
• formulaic contrasts: "It is not just about X; it is about Y";
• padded sections where every heading has the same length and rhythm;
• conclusions that encourage instead of deciding;
• missing concrete details: no filenames, commands, status values, URLs, check names, or failure modes.

Here is a small rewrite example.

Before:

In today's rapidly evolving AI landscape, teams must leverage robust review processes to ensure seamless publication of high-quality content that resonates with audiences.

After:

Do not publish an AI-assisted technical article until a reviewer can point to the brief, the source map, the original artifact, and the build output.

The second version is not more casual. It is more inspectable. A reviewer can check whether those artifacts exist.

Static-site technical checks

Editorial approval is not enough for a static content site. The article also has to pass the build and publication gates.

For an Astro-style Markdown site, use a short checklist like this:

Pre-publication draft checks
- Draft lives in drafts/<section>/, not content/<section>/.
- Frontmatter has title, description, date, slug, section, tags, author, reviewed_by, ai_assisted, affiliate_disclosure, sources, original_value, risk_level, and status.
- status is draft until publication approval.
- Source URLs are present and reachable.
- Internal links point to existing pages.
- No affiliate or monetized recommendation is present unless the review gate allows it.
- pnpm run ci passes.
- The draft slug and title are absent from dist/.
- The draft slug and title are absent from src/generated/articles.json.

Publication checks
- Move only the accepted file into content/<section>/.
- Set status: published.
- Re-run pnpm run ci.
- Confirm generated article count is expected.
- Confirm sitemap and RSS include the article only after publication.
- Deploy with the required public build-time environment variables.
- Confirm production article, section index, sitemap, and RSS contain the expected title or slug.

This is the same principle as status checks in software development. A passing build does not prove the article is good. A good review does not prove the site output is correct. You need both.

Reviewer prompt template

Use this as a PR comment, issue comment, or reviewer-agent prompt.

Review the AI-assisted technical article against the brief, research packet, and publishing workflow.

Return one verdict: APPROVED, REQUEST_CHANGES, or BLOCK.

Check these gates separately:
1. Assignment fit
2. Fact/source support
3. Source quality and caveats
4. Original value
5. Editorial clarity and anti-AI tone
6. Technical publishing readiness
7. Disclosure, monetization, and risk claims

Verdict: APPROVED | REQUEST_CHANGES | BLOCK

Blocking issues:
- ...

Required edits:
- ...

Fact/source concerns:
- ...

Original-value concerns:
- ...

AI-tone concerns:
- Quote suspect text and explain the tell.

Technical acceptance notes:
- CI/build:
- Internal links:
- Draft path safety:
- Generated article index:
- Sitemap/RSS:
- Production body check:

Notes for the writer:
- ...

For agentic workflows, the reviewer should be separate from the writer. In the planner-executor-reviewer workflow, the reviewer does not simply polish the executor's output; it decides whether the work can move forward. In a research-writing-review pipeline, the reviewer should check the draft against the research packet rather than trusting the draft's own citations.

What to do when the draft fails

Failure should create a concrete next action.

If the problem is factual, ask for a narrower claim or a better source. If the source does not support the claim, remove the claim. Do not keep it because it sounds plausible.

If the problem is source quality, ask for a primary source, a caveat, or a rewrite that makes the limitation visible. Vendor documentation is useful for explaining a product's features. It is weak evidence for broad claims about productivity, revenue, rankings, or quality outcomes.

If the problem is original value, ask for an artifact. A technical article can add value with a checklist, comparison table, command sequence, folder structure, test matrix, reviewer prompt, or decision tree. A summary alone is usually not enough.

If the problem is tone, quote the passage and name the tell. "Make this sound human" is not a useful review comment. "This paragraph uses vague authority and inflated outcome language; rewrite it around the actual file, check, and failure mode" is actionable.

If the problem is technical publishing, stop publication. A broken build, draft leak, wrong sitemap, missing RSS item, or stale production body is not an editorial nit. It means the release state is unknown. The draft-safety process in How to Prevent AI Agents from Publishing Drafts by Accident exists for this reason.

When to publish, revise, or discard

Use plain rules.

Publish when:

• the draft satisfies the brief;
• important claims are sourced or narrowed;
• source limitations are visible;
• the article includes a useful original artifact;
• anti-AI-tone issues are resolved;
• CI/build checks pass;
• public output matches the intended publication state.

Request changes when:

• the article is useful but needs better sources, clearer examples, stronger artifacts, or plainer prose;
• metadata, internal links, or static-site checks have small fixable issues;
• the conclusion or opening is generic but the core article is sound.

Discard or restart when:

• the article has no clear reader outcome;
• the draft is mostly a rephrasing of public sources;
• the central claim cannot be supported;
• the article requires a level of legal, financial, medical, or security review the team cannot provide;
• fixing the draft would take more effort than writing from the brief again.

A good review gate slows down bad publishing. It should not slow down every sentence edit. The point is to make the publish decision visible, repeatable, and tied to evidence.

Sources

• Google Search Central: Creating helpful, reliable, people-first content — https://developers.google.com/search/docs/fundamentals/creating-helpful-content
• Google Search Central: Spam policies for Google web search — https://developers.google.com/search/docs/essentials/spam-policies
• Google Search Central Blog: Google's guidance about AI-generated content — https://developers.google.com/search/blog/2023/02/google-search-and-ai-content
• Google Search Central: SEO Starter Guide — https://developers.google.com/search/docs/fundamentals/seo-starter-guide
• W3C Web Content Accessibility Guidelines 2.2 — https://www.w3.org/TR/WCAG22/
• GitHub Docs: About pull request reviews — https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/about-pull-request-reviews
• GitHub Docs: About status checks — https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/collaborating-on-repositories-with-code-quality-features/about-status-checks
• NIST AI Risk Management Framework — https://www.nist.gov/itl/ai-risk-management-framework
• OWASP Top 10 for LLM Applications — https://genai.owasp.org/llm-top-10/