🎮 The Next Input — Issue #093

In partnership with

⚡ The Briefing — 60 sec

• Redfern Station’s AI-generated Christmas artwork to be removed
  Could just be me, but this feels like a reach. Not every QR code and squiggly line is a culture war.

• US prosecutor slammed for filing AI-generated motion full of inaccuracies
  This one’s not a reach. This is just sloppy. If you’re filing legal motions with zero proofreading, that’s a you problem.

• 49 US AI startups raised $100M+ in 2025
  Forty-nine companies, each pulling nine figures. That’s not a wave—that’s capital doing laps.

🛠️ The Playbook — The Risk Scanner: Daily Red-Flag Monitor for Your Org

Mission Automatically detect questionable AI outputs, compliance risks, and accuracy issues before they hit clients or leadership.
Difficulty Medium
Build time 2.5 hours
ROI Cuts human review by 60% and stops “AI-made us look dumb” moments before they happen.

0) Why This Matters

AI output is everywhere—emails, briefs, legal drafts, client messages.
But as the prosecutor story shows, you only need one sloppy AI-generated document to create a real problem.
This scanner acts as your quality filter: catches hallucinations, misquotes, legal flags, tone mismatches, and factual inconsistencies before anything ships.

1) Architecture

2) Workflow

1. Create a shared inbox or Slack channel where drafts are dropped.

2. Automation picks up the doc and sends it to Claude 4.5 Haiku.

3. Haiku checks:

   • factual inconsistencies

   • hallucination hotspots

   • tone errors

   • legal/compliance flags

   • missing citations

4. GPT-5-mini runs a secondary pass for contradictions or mismatched claims.

5. Both outputs merge into a simple “Risk Report”:

   • red = incorrect

   • amber = uncertain

   • green = clean

6. Author receives the report with suggested edits.

3) Example Prompts

Primary Scan Prompt (Claude 4.5 Haiku)

Scan this draft for:
- factual inaccuracies
- unsupported claims
- contradictions
- legal or compliance issues
- tone mismatches
Provide:
- a red/amber/green rating
- corrections or required rewrites
Keep it concise and confident.

Verification Prompt (GPT-5-mini)

Cross-check all factual claims in this draft.
Flag:
- contradictions
- ambiguous statements
- claims that cannot be validated
Return a short list of required fixes.

4) Guardrails

• Do not auto-correct legal content—flag only.

• Keep scans internal; never forward client material externally.

• Cap scan size for very long documents to avoid false positives.

• Require human confirmation before applying any suggested edits.

5) Pilot Rollout — 3 hours

1. Build a “Draft Review” Slack channel.

2. Connect a Make.com scenario to watch the channel.

3. Run 10 internal docs through the scanner to calibrate rules.

4. Review all red/amber flags with the team.

5. Finalize the Airtable ruleset.

6. Turn on automated daily scanning.

6) Metrics

• Number of drafts scanned per week

• Percentage flagged red or amber

• Average time saved per review

• False-positive rate

• Reduction in client-facing corrections over time

Pro Tip: Add a “tone consistency” rule. Half the issues in AI-generated text aren’t factual—they’re vibe-breaking.

🎯 The Arsenal — Tools & Prompts

• Claude 4.5 Haiku · Fast, accurate risk scanning · https://anthropic.com

• GPT-5-mini · Lightweight verification + contradiction checks · https://openai.com

• Airtable · Rule store + change log · https://airtable.com

• Make.com · Automate draft intake → scan → report · https://make.com

Copy-paste prompt block:

You are my risk scanner.
Flag any:
- factual errors
- contradictions
- compliance issues
- tone mismatches
Provide a simple red/amber/green status and fixes.

💡 Free Office Hours

Want help implementing anything? Book a free 15-minute Office Hours slot—no sales pitch, just workflows solved.

→ https://calendly.com/aaron-cylentis/the-next-input-office-hours

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🕹️ Game Over

Don’t trust the draft—trust the scanner.

— Aaron Automating the boring. Amplifying the brilliant.

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