Editorial Policy

Last updated: March 1, 2026

This page explains how Ask AI plans, writes, reviews, and updates editorial content. Our objective is simple: publish practical, original pages that help users complete real tasks.

1. Content scope and intent mapping

Each guide is assigned one primary task intent and a dedicated long-tail cluster. We avoid publishing multiple pages that target the same user intent with superficial wording changes.

One URL, one task outcome: each indexable guide has a distinct workflow objective.
No doorway pages: we do not publish near-duplicate pages to capture trivial keyword variants.
Search intent fit: every page must answer a practical user question with executable steps.

2. Originality and quality requirements

We publish content only when it adds clear value beyond generic AI advice. Articles must include structured workflows, concrete examples, and verification guidance.

Required components: workflow steps, reusable patterns, and quality checks.
Clear language: direct writing for practical decision-making.
No keyword stuffing: natural language is mandatory across headings and body copy.
No thin pages: pages without sufficient original value are not indexed.

3. Human review process

AI may assist with draft acceleration, but final publication is controlled by human editorial review. Review checks focus on clarity, factual overreach, repetition risk, and real-world usefulness.

Editorial review: structure, readability, and intent fit.
Risk review: explicit responsible-use statements for high-impact topics.
Consistency review: remove duplicated sections and template-like phrasing.

4. Corrections policy

If a user reports a factual issue or unclear recommendation, we review and correct it. Corrections are prioritized when they affect decisions, safety, or user trust.

How to report: email support@askai.ws with URL, issue, and evidence.
Correction target: initial response within 2 business days.
Update transparency: major edits are reflected in page update dates.

5. Update cadence

We update guides when workflows, platform behavior, or policy context changes. We also run periodic audits for duplication, low-value sections, and stale examples.

6. Evidence and source handling

When an article includes factual statements that can affect operational decisions, we require source-aware writing. Authors must distinguish between practical guidance, assumptions, and verifiable facts. If a recommendation depends on external constraints, those constraints must be stated clearly instead of implied.

We do not present uncertain claims as confirmed facts. If evidence is incomplete, the page must indicate uncertainty and provide a verification step. This is especially important for legal, medical, financial, and security-adjacent scenarios. In these categories, our editorial rule is conservative: explain workflow boundaries and direct users to qualified experts.

7. AI usage disclosure

Ask AI may use AI-assisted drafting in early content development, but publication is never fully automated. Human editors are responsible for final structure, risk language, and quality checks before release. We do not publish raw model output as finished editorial content.

AI assistance is used to accelerate ideation, outline generation, and candidate examples. Human reviewers then remove weak sections, add operational context, and enforce quality standards. This process is designed to prevent repetitive boilerplate and low-value pages while preserving speed in editorial production.

Draft phase: AI can propose structure and examples.
Review phase: editors check factual risk, clarity, and usefulness.
Publish phase: pages are released only after human approval.

8. User feedback and accountability loop

Editorial quality is monitored with user feedback, support tickets, and periodic quality audits. We review reports of ambiguity, factual risk, and workflow gaps, then prioritize updates by user impact. Pages with recurring quality concerns are revised or removed from indexation until quality improves.

Our accountability loop is simple: detect issue, validate issue, apply correction, verify result. This loop helps prevent accumulation of stale or low-value content and supports long-term trust signals for users, search engines, and policy reviewers.

9. Editorial independence and monetization

Advertising and analytics do not determine editorial conclusions. We do not publish content solely to increase page count or ad inventory.

Editorial quality decisions prioritize user usefulness, clarity, and trust over short-term traffic tactics.

10. Methodology implementation reference

This policy defines what standards we enforce. The operational implementation of those standards is documented in Editorial Methodology, including evidence hierarchy, workflow validation protocol, quality rubric, and maintenance triggers.

Policy without execution detail is insufficient for quality governance. We publish both pages so users and reviewers can evaluate not only our principles, but also our repeatable execution process.