Perceived Authorship And Trust Management

Issue 60 Edition 2026-03-01 4 min read

Not accepted General

Sources: 1 • Confidence: Medium • Updated: 2026-04-12 10:14

Key takeaways

The author reports that many readers assume his blog writing is partially or fully created by LLMs because he often writes about LLMs.
The author uses an LLM to update code documentation or draft a project README, and then edits the output to remove opinions and invented rationale statements.
The author uses LLMs to proofread blog text that he publishes and says he has shared his current proofreading prompt.
The author does not allow LLMs to speak on his behalf in a first-person or opinionated voice.

The author reports that many readers assume his blog writing is partially or fully created by LLMs because he often writes about LLMs.
The author does not allow LLMs to speak on his behalf in a first-person or opinionated voice.

The author uses an LLM to update code documentation or draft a project README, and then edits the output to remove opinions and invented rationale statements.
The author uses LLMs to proofread blog text that he publishes and says he has shared his current proofreading prompt.

What exact disclosure practices does the author use (e.g., per-post notes, a standing policy page), and how consistently are they applied?
What is the author’s proofreading prompt, and what changes (if any) has it undergone over time?
How often are LLMs used for documentation/README drafting, and what portion of final published documentation typically originates from an LLM draft?
Does the author have a defined accuracy verification process for AI-assisted documentation beyond removing opinions/invented rationale (e.g., technical fact-checking checklist)?
Did the author observe any measurable change in reader confusion or trust after communicating this AI-writing policy?

Rising demand for tooling and workflows that help creators disclose AI assistance, preserve authentic voice, and manage reader trust.
Growing need for AI assisted documentation and README drafting solutions paired with human review and redaction controls to prevent invented rationale or opinions.
Increased emphasis on governance features in LLM products that restrict first person or opinionated generation and support auditable human editing steps.

The author publishes a consistent disclosure policy across posts and reports reduced reader confusion about AI authorship.
The author shares a stable proofreading prompt and describes a repeatable process for removing opinions and invented rationale from LLM drafts.
The author adds or references a formal accuracy verification step for AI assisted documentation beyond stylistic redaction and proofreading.

No clear or consistent disclosure practice is adopted, and perceived AI authorship confusion persists or worsens.
LLM use expands into first person or opinionated writing, contradicting the stated boundary and undermining trust framing.
Repeated errors appear in AI assisted documentation, suggesting the redaction approach does not prevent misinformation.