Monthly Source Audit: Maintaining Content Accuracy

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Sources verified Dec 25, 2025

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Scenario

Context: You maintain a content platform where all claims must be source-backed. Sources can become stale.

Goal: Learn a systematic workflow for auditing source accuracy using AI tools

Anti-pattern: Letting sources go stale, claims diverge from original sources, or content reference outdated statistics

Tools: Claude Codenpm audit scriptsgit

Conversation

Why Monthly Audits Matter

Source-backed content drifts:

• URLs break (404s, redirects)
• Statistics get updated (2024 report → 2025 report)
• Claims are copied with slight variations
• Content references old source versions

A monthly audit catches drift before it compounds.

👤 You

I need to run a comprehensive source accuracy audit. Start by assessing current state.

Run the full audit and give me a prioritized work plan.

Start with assessment, not fixes. Understand scope before diving in.

🤖 AI

Running the full audit to assess current state.

Audit Report Summary

Check	Status	Action Needed
Source Staleness	3 stale	Verify URLs, update dates
Coverage Gaps	1 HIGH	Add missing citations
Citation Consistency	18 warnings	Review for accuracy
Content Staleness	5 pages	Re-review after source updates

Prioritized Work Plan

Day 1: Triage

1. Fix the 1 HIGH coverage gap (missing source citations)
2. Check 3 stale sources - verify URLs still work

Day 2: Verification
3. For each stale source: fetch URL, compare to cached content
4. Update last_verified dates and key_findings if changed

Day 3: Propagation
5. If any statistics changed, find ALL references and update
6. Run consistency check to verify no mismatches

Day 4: Validation
7. Run build validation
8. Update baseline snapshot
9. Commit with detailed changelog

Multi-day structure for thorough review. Prioritize by severity.

👤 You

The DORA 2025 report updated a statistic. Old: '90% adoption'. New: '89% of organizations prioritizing AI integration'. Find and update all references.

Statistic migration - a common audit task.

🤖 AI

I'll help migrate this statistic change.

Step 1: Find All References

Searching for the old claim...

Found 2 matches in 1 file:
  recommendations/single_dimension_rules.json
    Line 8: "With 90% industry adoption..."
    Line 44: source reference to dora_2025

Step 2: Update Content

Before:

"finding": "With 90% industry adoption in 2025..."

After:

"finding": "With 89% of organizations prioritizing AI integration in 2025..."

Step 3: Update Source File

Also updating the source's key_findings to reflect the accurate stat.

Step 4: Verify No Orphaned References

Running consistency check... confirmed: no other files cite "90%" for this source.

Step 5: Validate

✓ All schemas valid
✓ Build successful

Ready to commit:

fix: correct DORA stat 90% → 89% (accurate citation)

Always update both the content AND the source file.

Deep Verification Workflow

For thorough source verification, check:

1. URL accessibility - Does primary URL still work?
2. Content accuracy - Do key_findings match current content?
3. Quote verification - Are quoted passages still accurate?
4. Version updates - Is there a newer version available?
5. Citation propagation - Are all content references consistent?

👤 You

Verify the 'veracode_2025' source thoroughly. Check if URL works, content is accurate, and all citations are consistent.

Individual source deep verification.

🤖 AI

Source Deep Verification: veracode_2025

1. URL Check

URL: https://www.veracode.com/state-of-software-security-2025
Status: 200 OK ✓

2. Key Findings Verification

Comparing cached key_findings to live content:

Cached Finding	Live Content	Status
"AI-generated code introduced security flaws in 45% of tests"	Confirmed on page	✓ Match
"AI outputs include 3 different CWE categories"	Confirmed	✓ Match

3. Quote Check

"AI-generated code introduced security flaws in 45% of tests"

Searching page... Exact match found ✓

4. Version Check

Current version: "State of Software Security 2025"
No newer version detected.

5. Citation Consistency

Places citing this source:

• dimensions/trust_calibration.json - "45%" ✓
• conversations/anti_patterns/vibe_coding_failures.json - "45%" ✓
• rules/trust_rules.json - "45%" ✓

All citations consistent.

Result

VERIFIED - Updating last_verified to today's date.

Systematic verification catches drift before it compounds.

Key Takeaways

• Audit sources monthly to catch drift before it compounds
• Prioritize by severity: HIGH coverage gaps before LOW consistency warnings
• When statistics change, update BOTH the source file AND all content references
• Deep verification checks: URL, content accuracy, quotes, versions, consistency
• Always validate the build after making changes

Try It Yourself

Prompt Template

Run a source audit on your documentation:

1. List all external sources/references
2. For each: verify URL works and content is current
3. Check if any statistics or quotes have been updated
4. Update your content to match current sources

Copy template

Variations to Try

• For a single source: 'Verify [source] - check URL, content accuracy, and all places it's cited'
• For statistic migration: 'The stat changed from X to Y. Find and update all references.'
• For broken links: 'Find alternative URLs or archive.org snapshots'

Related Content

📖 AI System Monitoring 📖 Calibrated AI Use

Sources

✓ Google DORA: Source maintenance example ⓘ

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State of AI-Assisted Software Development 2025

Google DORA

The 2025 DORA report introduces the 'AI Capabilities Model' identifying seven practices that amplify AI benefits. The core insight is that AI is an 'amplifier' - it magnifies existing organizational strengths AND weaknesses. Key stats: 89% of orgs prioritizing AI, 76% of devs using daily, but 39% have low trust. The trust research is critical: developers who trust AI more are more productive, but trust must be earned through organizational support (policies, training time, addressing concerns). The 451% adoption increase from acceptable-use policies is remarkable - clarity enables adoption.

Key Findings:

• 89% of organizations prioritizing AI integration into applications
• 76% of technologists rely on AI for parts of their daily work
• 75% of developers report positive productivity impact from AI

Credibility: high

Referenced by:

Debug a Hallucinating AI Monthly Source Audit: Maintaining Content Accuracy

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