An evidence-first content standard

My content problem was not a lack of ideas

At one point I had several overlapping sources of content ideas: a generated idea collection, a separate social pack, a content calendar, automated daily suggestions, project notes, and several partial drafts.

Each source contained useful material. Together they created a governance problem:

• the same lesson appeared under several titles,
• project status could change without the copy changing,
• one draft might call a design “live,”
• internal architecture could leak through an otherwise useful story,
• and generic content could move faster than the posts grounded in real work.

The first audit made the issue concrete in a way I did not expect. A backlog labelled with one item count actually held noticeably more, because entries had been appended without the label being updated. Only a minority of them had ever received an explicit competitive-landscape review, even though the register's structure implied all of them had. A later project-by-project inventory multiplied the total again.

The lesson was not that more ideas were better. It was that counts and labels were being mistaken for editorial completion — the register was describing its own tidiness rather than the state of the work.

I needed one source of truth and a definition of done.

The two states every draft needs

I now separate proof state from publication state.

Proof state

• Live: implemented and currently observable.
• Built: implemented or tested, but not necessarily running now.
• Historical: a dated incident or completed change with surviving evidence.
• Source: code or documentation exists, but runtime behavior is not proven.
• Design: planned or specified only.
• Open: a real unresolved question worth exploring in public.

Publication gate

• Green: normal fact-checking and copy review should be sufficient.
• Amber: useful, but it needs fresh verification, sanitization, owner context, or a stronger caveat.
• Red: publish the generalized method only; keep the underlying operational evidence private.

These labels answer different questions. A system can be Live and still Red because publishing its details would create risk. A Design can be Green if the article clearly presents it as a proposed framework instead of a delivered result.

Most importantly, Green is not auto-publish authority. It means the draft is a good candidate for final human review.

One active post at a time

Bulk generation was useful for discovering themes. It was poor at finishing articles.

When several drafts were expanded in parallel, the language became repetitive, the same references appeared regardless of topic, and the implementation details received less scrutiny. The process now permits one active post.

For each draft:

1. inspect the underlying project, source documents, history, and current state;
2. list the factual claims the article intends to make;
3. label each claim as observed, documented, reported, inferred, or proposed;
4. remove or qualify anything the evidence cannot support;
5. research external claims with current primary or authoritative sources;
6. write a substantial canonical website article;
7. create a technical and/or business LinkedIn derivative that stands on its own;
8. add references actually used and a short further-reading path;
9. perform privacy, security, rights, and commercial-claims review;
10. validate the built review copy before it reaches a public channel.

Only then does the queue move.

The six-part public-content standard

1. Reader and decision

Every piece names one primary reader and one problem, question, or decision.

“Anyone interested in AI” is not an audience. “An internal IT leader deciding whether an agent should receive write access” is.

This does not mean excluding everyone else. It means giving the article enough specificity to be useful.

2. Firsthand contribution

The article must add something that could not be obtained by summarizing the first page of search results:

• an implementation choice,
• a failure and its cause,
• a tested constraint,
• a decision among alternatives,
• a sanitized artifact,
• or a reasoned opinion clearly labeled as such.

This aligns with Google's people-first guidance, which asks whether content provides original analysis, demonstrates firsthand expertise, and leaves the reader able to achieve a goal. Google also warns against scaling automated pages that add little value.

3. Claim-appropriate evidence

Not every useful article needs a measurable outcome.

A benchmark post needs test conditions and results. A security guide needs a threat model, negative tests, and authoritative technical references. A business opinion needs clear reasoning and boundaries. A customer-performance claim needs substantially stronger proof and permission.

The rule is: use the kind of evidence the claim requires.

This matters commercially in Canada. The Competition Bureau advises businesses not to make performance claims unless they can prove them, and warns against distorting tests or testimonials. A successful lab run does not support a broad claim about customer results.

4. Limits beside the claim

The limitation should appear near the claim, not in a footnote designed to be missed.

Useful limitations include:

• tested on one environment,
• source exists but deployment is not proven,
• synthetic example, not customer evidence,
• self-reported result, not independently verified,
• historical behavior, not current runtime state,
• design recommendation, not legal or security assurance.

This makes the article more credible and reduces the chance that derivative LinkedIn copy outruns the canonical version.

5. Public-safety and rights review

Before release, remove or generalize:

• credentials, recovery material, and secret-shaped values;
• private hostnames, addresses, ports, routes, and filesystem coordinates;
• access-policy identities and precise security gaps;
• customer, recruiter, former-employer, or personal information without rights and permission;
• screenshots, logs, traces, prompts, or dashboards that reveal more than the teaching point;
• current vulnerabilities or operational timing that would help an attacker;
• third-party text, images, or testimonials without suitable rights.

When a real artifact is too revealing, rebuild the smallest useful version with synthetic data and label it as synthetic. Do not imply that synthetic evidence proves customer value or production readiness.

6. One honest next step

Every article should give the reader somewhere useful to go next, but it does not need a sales pitch.

The next step could be:

• apply a checklist,
• inspect a public reference project,
• read the primary standard,
• compare the method with their own workflow,
• share a real counterexample,
• or contact me if the specific problem is relevant.

LinkedIn says it is reducing generic, recycled, click-driven posts and engagement bait. “Comment YES if you agree” is not a substitute for value. A CTA should fit the article and the reader's stage.

References and further reading serve different purposes

I use two source blocks.

References contains sources that directly support claims made in the article. If I say what a protocol requires, what a regulator advises, or what a platform policy permits, the corresponding primary source belongs there.

Further reading is a short curated path for someone who wants to go deeper. A source does not move into References merely because it is reputable; it must support something the article actually says.

This distinction prevents decorative citation lists.

How I handle AI-assisted drafting

AI is useful in this process for:

• searching a large private evidence base,
• finding contradictions and missing topics,
• locating candidate primary sources,
• outlining alternative explanations,
• generating adversarial review questions,
• and producing first-draft derivatives.

It does not receive final authority over:

• factual status,
• privacy and disclosure decisions,
• legal or commercial claims,
• whether an operational detail is safe,
• whether the prose represents my experience and view,
• or public release.

LinkedIn's current guidance encourages members to review, edit, and approve AI-assisted content, makes the member responsible for what is posted, and recommends disclosure when content relied heavily on AI and that reliance is not obvious. It also emphasizes that useful AI-assisted content should still reflect the person's own perspective and experience.

My practical disclosure rule is contextual:

• disclose substantial AI drafting or transformation when a reasonable reader would care how the piece was made;
• always disclose synthetic demonstrations;
• do not present model-generated experience or opinion as mine;
• and keep a private record of the sources and human edits behind the final version.

What I will not automate

I may automate register maintenance, link checks, formatting, build validation, and draft preparation.

I will not automate:

• final publication without my review,
• comments written as if they were my spontaneous response,
• engagement pods or reciprocal engagement schemes,
• unsolicited outreach triggered only by post interaction,
• claim upgrades from Design to Built or Built to Live,
• or reuse of a person's story, testimonial, or data without permission.

LinkedIn explicitly says automated comments and engagement pods are not allowed and may lead to reduced distribution or account restrictions. More importantly, they would undermine the evidence-first position the content is meant to build.

The release checklist

Before a post leaves the private review hub, I ask:

Value

• Is the intended reader clear?
• Does the piece solve a real problem or improve a real decision?
• Is there firsthand value beyond a summary of other sources?
• Can the reader apply the lesson without seeing my private environment?

Evidence

• Is the proof state current?
• Does every consequential factual claim have support?
• Are observations, inferences, and recommendations distinguishable?
• Are test conditions and known skips stated?
• Are dates attached to runtime-sensitive claims?

Safety and rights

• Are secrets and operational coordinates removed?
• Are personal, employer, customer, and recruitment data protected?
• Are screenshots and examples synthetic or explicitly authorized?
• Are quotations, images, and testimonials within my rights to publish?

Commercial accuracy

• Does the post avoid implying unproven customer outcomes?
• Are qualifications, service availability, prices, guarantees, and support levels current and approved?
• Does the CTA match what I can genuinely deliver now?

Authorship and channel

• Does this sound like me?
• Have I reviewed and accepted every line?
• Is AI assistance disclosed when appropriate?
• Is the website version canonical and the LinkedIn version useful on its own?
• Is follow-up owned by a person?

Measuring whether the standard helps

Impressions alone do not tell me whether the content is doing useful work.

I would track a small set of first-party outcomes:

• qualified replies or messages,
• readers who use or save the public artifact,
• relevant conversations started,
• inquiries that match the stated service boundary,
• corrections or counterexamples that improve the article,
• and eventually work or opportunities with a traceable content connection.

The goal is not to claim that the standard automatically improves conversion. It is to create enough consistent metadata to learn which topics and formats lead to useful conversations.

The business lesson

Publishing standards are not a way to make every post sound institutional. They are a way to keep a personal voice from accidentally making institutional- sized claims.

The content should still be candid, specific, and recognizably mine. The system exists so that a useful story does not become a misleading case study, a private diagram does not become an attack map, and an AI-assisted draft does not become an opinion I never actually formed.

That is the standard I want attached to my name before I ask content to create business trust.

References

• LinkedIn: Best practices for content created with the help of AI — member responsibility, review, rights, substance, voice, and contextual disclosure guidance.
• LinkedIn: Supporting authentic content and conversations — platform action against automated comments and engagement pods.
• LinkedIn: Improving the feed — reduced distribution for generic, recycled, and engagement-bait content and emphasis on actionable professional perspectives.
• Google: Creating helpful, reliable, people-first content — original value, firsthand expertise, sourcing, authorship, and contextual explanation of automation.
• Competition Bureau Canada: False or misleading representations — substantiation of performance claims and proper use of tests and testimonials.

Further reading

• Google: Guidance on generative AI content — accuracy, quality, relevance, context, and scaled-content concerns.
• Competition Bureau Canada: Influencer marketing and the Competition Act — clear disclosure of material connections and limits on broad performance claims.
• Office of the Privacy Commissioner of Canada: Limiting Collection — collect only the personal information needed for an identified purpose.
• NIST AI Risk Management Framework Core — documented limitations, human oversight, and contextual interpretation of AI output.