Editorial Standards - AI Error Hub

Editorial Standards Sections

Our 8 Editorial Principles
The Error Documentation Process
Code Testing Standards
Citations and Sources
Bylines and Attribution
Content Updates and Freshness
Correction Policy
Conflict of Interest Disclosures
Our Use of AI in Content Creation
Accuracy and Fact-Checking
Reader Feedback and Contributions

1. Our 8 Editorial Principles

These principles govern every page we publish. If content violates any of them, it doesn't ship.

1 Working Code Above All

Every error page includes code that actually works. If we can't get the fix code to run successfully, the page doesn't get published. No pseudo-code, no theoretical fixes, no "should work" language.

2 Reproduce Before Documenting

We reproduce every error in our own test environment before writing about it. If we can't trigger the error, we can't confidently explain the fix.

3 Cite Primary Sources

Every claim about API behavior links to official provider documentation. We prioritize primary sources over Stack Overflow, Reddit threads, or other blogs.

4 Freshness Over Comprehensiveness

A 200-word tested-yesterday page beats a 2,000-word outdated page. We show "Last Tested" dates prominently and retest every page every 60 days.

5 Human Bylines on Everything

Every error page shows the researcher who tested the fix. Bylines aren't decorative — they're accountability. Our researchers put their names on their work.

6 Corrections Fast, Corrections Public

When we're wrong, we update the page immediately and add a correction note at the bottom. We don't silently edit — we document what changed and why.

7 Disclose All Financial Relationships

Affiliate links marked. Sponsored content labeled. Direct sponsors named. Our financial incentives are visible to every reader.

8 Speak Like a Peer, Not a Corporation

We write for developers as developers. No marketing fluff. No "leveraging solutions." Just: this is the error, here's what causes it, here's the fix, here's the code.

2. The Error Documentation Process

Every error page goes through this workflow before publication:

Identify

Error spotted in changelogs, community reports, or our team hitting it

Reproduce

Researcher recreates the exact error in a test environment

Investigate

Root causes identified via provider docs, source code, and testing

Test Fixes

Multiple fix approaches tested; working ones documented with code

Draft

Page written using our template (Quick Fix, Root Cause, Fixes, Prevention, FAQs)

Peer Review

Second researcher re-tests every code sample and verifies claims

Editor Review

Content Editor checks accuracy, clarity, internal linking, schema markup

Publish

Page goes live with tested code, byline, and Last Tested date

3. Code Testing Standards

Code examples are the highest-liability part of any error page. Here's how we ensure every code snippet works:

Every Code Sample Meets These Requirements

Tested against the current stable SDK version at time of publication
Runs in an isolated test environment before being pasted into the page
Actually triggers the described error (not just theoretical)
Fix code is executed and confirmed to resolve the error
Environment variables and API keys are anonymized (never real keys)
Includes basic error handling even when demonstrating the error
Language and SDK version noted in a comment header
Verified working on macOS, Linux, and Windows (when applicable)

Supported Languages and SDKs

We prioritize the most common developer environments:

Language	Coverage	Testing Frequency
Python	All major providers, primary language	Every 30 days
TypeScript / JavaScript	All major providers, primary language	Every 30 days
cURL / HTTP	Universal for all API-based providers	Every 60 days
Go	Providers with official Go SDKs	Every 90 days
Rust	Community SDKs where relevant	Every 90 days

4. Citations and Sources

We follow a strict source hierarchy for every factual claim:

Primary sources (highest trust): Official provider documentation, changelog entries, SDK source code, provider engineer blog posts
Secondary sources (good trust): Provider-endorsed community forums (Anthropic Discord, OpenAI community), GitHub issues opened by provider engineers
Tertiary sources (verified only): Stack Overflow answers, Reddit threads, other blogs — only cited when we've independently verified the claim
Our own testing (equal to secondary): When we reproduce and confirm behavior that isn't documented, we clearly note this with "Our testing confirmed..." language

Source Freshness Rules

Provider docs must be from within the last 60 days
SDK versions cited must be from the latest stable release channel
Community forum discussions from the last 90 days are preferred
If a source is older than 6 months, we verify it's still accurate before citing

5. Bylines and Attribution

Every error page shows:

Tested By: Real name of the researcher who reproduced the error and verified the fix
Last Tested Date: Actual date of the most recent successful test
First Reported Date: When we first documented this error (for historical context)
Contributor credits: If a community member reported the error or contributed the fix, they're credited in a "Contributors" section

Why Bylines Matter

Bylines create accountability. Our researchers put their names on their work because it makes them careful. It also lets readers reach out with follow-up questions — every byline links to the researcher's professional profile.

6. Content Updates and Freshness

AI API landscapes shift weekly. Here's how we keep content current:

Retesting Schedule

Content Type	Retest Frequency
Top 100 most-visited error pages	Every 30 days
Anthropic, OpenAI, Gemini error pages	Every 45 days
Other provider error pages	Every 60 days
Long-form guides	Every 90 days
Interactive tools (accuracy check)	Every 30 days
Live status monitoring	Continuous (60-second checks)

Trigger-Based Updates

Beyond scheduled retesting, we update immediately when:

Provider announces API changes (new model, deprecated model, breaking change)
Provider posts an incident that affects error behavior
A community member reports the documented fix no longer works
SDK releases a major version with different error handling

7. Correction Policy

Our commitment to accuracy means being fast and transparent about mistakes.

When We Correct a Page

Correction is applied within 24 hours of confirming the error
A "Corrections" section is added at the bottom of the page
Each correction lists: what was wrong, what's correct now, when it was corrected
If the correction is significant, we email newsletter subscribers who received the incorrect version
The reader who reported the issue is credited (with permission) in the Corrections log

Correction Categories

Critical: Broken code, dangerous advice, security implications → fixed within 4 hours
Major: Incorrect fix, wrong root cause, outdated but published as current → fixed within 24 hours
Minor: Typos, formatting, broken links → fixed within 72 hours

Report corrections to info@aierrorhub.com with subject "Correction" for standard issues or "URGENT Correction" for critical ones.

8. Conflict of Interest Disclosures

Financial relationships that could bias our editorial:

Affiliate partnerships: We earn commissions on some outbound links. Full details in our Affiliate Disclosure
Advertising: Display ads via AdSense, Ezoic (future), Mediavine (future). Ads don't influence content
Direct sponsorships: Companies pay for sponsored placements on relevant category pages. These are labeled "Sponsored"
Newsletter sponsorships: Each newsletter issue may include one paid sponsor. Sponsors are clearly labeled
Provider relationships: We have no equity, board seats, or paid consulting arrangements with any AI provider

Editorial Firewall

Financial partners do not review our content before publication. They cannot request changes to how we cover their products. Our recommendation of a product is not influenced by whether we have an affiliate relationship with them.

9. Our Use of AI in Content Creation

We're transparent about how we use AI internally — because you deserve to know.

Where We Use AI

Page structure drafting: Claude helps outline the standard sections (Quick Fix, Root Causes, Fixes, Prevention) based on our template
Meta description optimization: Claude suggests SEO-optimized descriptions we then edit
Code review assistance: Claude reviews code samples for common mistakes
Translation drafts: When we localize content, Claude produces drafts humans then verify

Where We Don't Use AI

Fix verification: Every fix is human-tested. AI cannot confirm a fix works — only a human running the code can
Code samples: Every code snippet is written and tested by a human researcher
Last Tested dates: These reflect real human testing sessions, not AI check-ins
Bylines: Every byline is a real human who did the work
Editorial judgment: Decisions about what to cover, how to structure content, and what recommendations to make are all human

10. Accuracy and Fact-Checking

Before any page publishes, our Content Editor verifies:

Pre-Publish Accuracy Checklist

All external links resolve (no 404s)
All internal links point to real pages
Provider name and product name spelled correctly
SDK versions cited match latest stable release
Code samples pass linting for the target language
Pricing figures, if mentioned, match provider's official page
Rate limits, if mentioned, match provider's rate limit documentation
All quotes from provider documentation are verbatim
Screenshots (if any) are current and not misleading
Schema markup validates against Schema.org

11. Reader Feedback and Contributions

Our readers make AI Error Hub better every week.

How Readers Contribute

Corrections: Email info@aierrorhub.com with subject "Correction"
Missing errors: Report errors we haven't documented yet
Better fixes: Suggest improvements to documented fixes
New error patterns: Share errors you've hit that others might too
GitHub contributions: Full contribution guidelines on our public repository

How We Credit Contributors

Community contributors are credited by name (with permission) in the "Contributors" section of pages they helped improve. Significant contributions may also be featured in our weekly digest.

Our Commitment

These standards are what separate a genuine developer reference from AI-generated content farms. Every page you read on AI Error Hub was written, tested, and reviewed by real engineers who put their names on their work. If we ever slip below these standards, tell us — and we'll fix it.