The Judgement Stack

10 min read | Topic: How?

Here’s an uncomfortable truth: the professionals who will thrive in the AI age aren’t those who can prompt the best outputs. They’re the ones who can judge them.

Every day, millions of knowledge workers accept AI-generated content at face value—copy it into emails, paste it into reports, present it to clients. And every day, some percentage of that content is subtly wrong, contextually inappropriate, or confidently fabricated. The AI doesn’t know the difference. You have to.

This is the Judgment Stack: a layered system for evaluating AI outputs that gets more sophisticated as the stakes get higher. Think of it as your quality control infrastructure—the invisible scaffolding that separates professionals who use AI well from those who merely use it often.

Layer 1: Pattern Recognition Practice

Before you can judge AI outputs, you need to develop an intuition for how AI thinks—and where it consistently fails. This isn’t about becoming a machine learning expert. It’s about building the same kind of pattern recognition a seasoned editor develops for bad writing, or a veteran investor develops for questionable numbers.

Daily “AI BS Detector” Exercises

The goal here is simple: train your instincts through deliberate practice. Set aside 10 minutes daily to actively hunt for AI failures.

The Verification Habit

Pick one AI output from your day—any output—and verify it end to end. Not because you suspect it’s wrong, but because you’re building a muscle. Check the facts. Trace the logic. Look up the citations (if there are any). You’ll be surprised how often something that felt right turns out to be subtly incorrect.

Track what you find. Over time, you’ll notice patterns:

Which types of queries produce the most errors?
What topics does your AI confidently bungle?
Where does it fill gaps with plausible-sounding nonsense?

The Confidence Calibration Test

AI models express uniform confidence whether they’re stating that water is H₂O or inventing a historical event that never happened. Your job is to develop differential confidence.

Try this: Before verifying any AI output, rate your confidence in its accuracy from 1-10. After verification, compare your prediction to reality. Most people start with poorly calibrated confidence (overconfident on complex topics, underconfident on simple ones). With practice, your intuitions sharpen.

The “Write First” Comparison

Periodically, write your own answer before asking AI. Then compare. Where does the AI add genuine value? Where does it miss nuance you instinctively included? Where does it introduce errors you wouldn’t have made? This exercise reveals both AI’s strengths and your own expertise—the latter being easy to forget when you’re outsourcing everything.

Hallucination Hunting Protocols

AI hallucinations aren’t random—they follow patterns. Learn the patterns, and you can anticipate where the lies hide.

High-Risk Hallucination Zones

Some categories are hallucination magnets. Be extra vigilant whenever AI outputs include:

Specific citations (papers, books, articles): AI frequently invents plausible-sounding sources that don’t exist
Statistics and numbers: Especially percentages, dates, and quantities that make claims more persuasive
Proper nouns: Names, places, company names, product names—anything that should correspond to a real-world entity
Recent events: Anything near or past the model’s training cutoff
Niche expertise: The more specialized the domain, the more likely the AI is pattern-matching rather than knowing

The “Too Perfect” Test

Hallucinations often reveal themselves through suspicious perfection. Real information is messy—studies have limitations, experts disagree, history has gaps. When AI output is too clean, too comprehensive, too perfectly aligned with what you wanted to hear, investigate.

Ask yourself: Does this feel like information retrieved, or information constructed?

Verification Triage

You can’t verify everything (nor should you). Develop a triage system:

Always verify: Any claim you’ll stake your reputation on. Anything that will inform significant decisions. Anything that seems surprising.

Spot-check: Routine outputs where occasional errors are recoverable. Check enough to maintain calibration.

Trust (with caution): Low-stakes outputs in domains where you have enough expertise to notice obvious problems. Even here, stay alert.

Edge Case Collection Systems

Every AI has blind spots and failure modes. Build a personal database of them.

Your AI Failure Journal

Keep a running log of AI mistakes you encounter. Include:

The prompt or query
What the AI got wrong
Why it probably failed
What you should have asked differently

Over time, this journal becomes a practical guide to your AI’s limitations—far more useful than any generic “how to prompt” tutorial.

Organizational Sharing

If you work in a team, create a shared channel or document for AI failures. When someone discovers a hallucination or systematic error, they post it. This collective intelligence helps everyone calibrate faster than individual learning alone.

Layer 2: Quality Calibration

Recognizing bad output is defense. Quality calibration is offense—structuring your workflow to consistently produce excellent results.

The 10-60-30 Rule

Here’s a framework for allocating your effort across the AI-assisted creation process:

10% — Inspiration and Framing

The first 10% of your effort goes into the initial prompt and conceptualization. This is where you:

Define what you actually need (harder than it sounds)
Provide relevant context
Specify constraints, format, and tone
Include examples of what good looks like

Many people spend too little time here, then spend far more time fixing outputs that were doomed from the start.

60% — Generation and Iteration

The middle 60% is the back-and-forth with AI. This is where most people stop—but it shouldn’t be. During this phase:

Generate multiple variations
Ask for alternatives and improvements
Challenge the AI’s assumptions
Explore different angles

Don’t fall in love with the first output. The magic of AI is iteration speed—use it.

30% — Human Refinement

The final 30% is all you. This is where human judgment adds irreplaceable value:

Verify facts and claims
Add context only you possess
Adjust tone for your specific audience
Insert the messy human truths that AI smooths over
Remove the AI-isms that scream “a robot wrote this”

This 30% is what separates AI-assisted excellence from AI-generated mediocrity. Skip it, and you’re just a prompt jockey.

Before/During/After Review Rhythms

Quality calibration isn’t a single checkpoint—it’s distributed across your workflow.

Before: Intent Clarity

Before you prompt, answer these questions:

What will I do with this output?
Who will see it?
What are the consequences of errors?
What do I already know that the AI doesn’t?

This is the thinking that makes prompts effective and review efficient.

During: Active Monitoring

While iterating, maintain awareness of:

Is the AI actually addressing my need, or something adjacent to it?
Are there red flags worth investigating now rather than later?
Is this heading toward something I can use, or should I restart?

Sometimes the fastest path to a good output is abandoning a bad thread entirely.

After: Structured Review

When you have a candidate final output, review systematically:

Accuracy pass: Are the facts correct?
Logic pass: Does the reasoning hold?
Completeness pass: Is anything important missing?
Tone pass: Is this appropriate for the audience?
Attribution pass: If I publish this, can I defend it?

You won’t always do all five. But knowing what you’re not checking helps you understand your risk.

Human-in-the-Loop Checkpoints

Some decisions should never be delegated to AI—even AI with human review. Design your workflow with explicit checkpoints where human judgment is mandatory.

The “No Autopilot” List

Identify categories where you’ll always do more than review:

Communications to important relationships
Decisions with significant irreversibility
Content that represents your core expertise or values
Anything involving legal, medical, or financial advice
Sensitive personnel matters

For these, AI might draft, but you substantively rewrite.

Checkpoint Design

For important workflows, build in required stops:

Gate reviews: Output can’t proceed until a human approves
Time delays: Mandatory waiting period before sending/publishing
Second opinions: Another human reviews AI-assisted work

Yes, this slows things down. That’s the point. Speed isn’t the only value.

Layer 3: Ethical Filtering

The highest layer of the Judgment Stack addresses something AI can’t evaluate: whether an output should exist. This is about bias, values, and the downstream effects of what you create.

Bias Detection Frameworks

AI models encode the biases of their training data—and then scale them. Part of human judgment is catching these biases before they cause harm.

The “Flip Test”

When reviewing AI output about people or groups, mentally flip the subjects. Would this description of Group A sound appropriate if applied to Group B? Would you describe a man this way if describing a woman? A younger person if describing an older one?

The flip test reveals assumptions that feel natural but aren’t neutral.

The Missing Voices Audit

AI tends toward dominant perspectives—the views most represented in its training data. When reviewing AI analysis or recommendations, ask:

Whose perspective is centered here?
Whose perspective is absent?
If the affected parties reviewed this, would they recognize their reality?

This is especially important for content about communities you’re not part of.

The Confident Ignorance Check

AI can sound authoritative about topics it shouldn’t opine on at all. Watch for:

Firm recommendations in domains requiring human expertise
Definitive statements about contested issues
Confident claims about recent or rapidly changing situations
Universalizing from limited contexts

The “Grandmother Test” for AI Outputs

Here’s a simple heuristic with surprising power: Would you be comfortable if your grandmother saw this output with your name on it?

This isn’t about being conservative or avoiding controversy. It’s about accountability. Your grandmother test might include:

Could I explain why I produced this?
Would I stand behind it publicly?
Does it reflect my values, or just my deadline?
If this causes harm, am I prepared to own it?

AI gives us the power to create at scale. The grandmother test asks whether we should.

The 24-Hour Test

A variant for high-stakes content: Could I defend this output 24 hours from now, after the immediate pressure has passed? Sleep on important AI-assisted decisions when possible.

Value Alignment Audits

Beyond individual outputs, periodically audit your overall AI usage patterns.

The Portfolio Review

Monthly, review a sample of your AI-assisted work:

What patterns do you notice?
Are you using AI to avoid work you should be doing yourself?
Are you using AI in ways that align with who you want to be professionally?
Has AI made your work better, or just faster?

The Externalities Check

Consider the broader effects of your AI usage:

What skills are you losing through non-use?
How is your AI usage affecting others (colleagues, clients, communities)?
Are you transparent about AI involvement where it matters?

The Values Recalibration

AI capabilities change constantly. Periodically revisit your boundaries:

What will you never outsource to AI?
Where do you draw the line on AI-generated content?
How much verification is enough for your standards?

These aren’t questions with permanent answers. They’re questions to keep asking.

Building Your Stack

The Judgment Stack isn’t meant to be implemented all at once. It’s a framework for continuous development. Start with whatever layer addresses your biggest current gap:

If you’re frequently surprised by AI errors → focus on Pattern Recognition

If your outputs are inconsistent in quality → focus on Quality Calibration

If you’re uncertain about the ethics of your AI usage → focus on Ethical Filtering

Then expand from there.

The professionals who master this aren’t the ones who trust AI the most, or trust it the least. They’re the ones who’ve developed calibrated trust—knowing exactly when to rely on AI and when to rely on themselves.

That calibration doesn’t come from reading about judgment. It comes from practicing it, daily, deliberately, with the humility to acknowledge that you—like the AI—will sometimes get it wrong.

The difference is that you can learn from your mistakes. Make sure you do.