How to Measure Real Skills vs AI-Powered Answers

TL;DR

AI can now:

• Generate polished explanations

• Produce working code

• Provide structured behavioral responses

• Outline system architectures

So answer quality is no longer the primary hiring signal.

To measure real skill, you must evaluate:

• How candidates think

• How they adapt

• How they handle uncertainty

• How they explain decisions

This is the reasoning signal, and it cannot be outsourced to AI.

The Root Problem

If your interview only measures:

• Correctness

• Confidence

• Fluency

then AI can pass the interview without the candidate having the skill.

To measure real ability, you must make the interview measure thinking, not remembering.

The Core Framework

Real skill can be measured using three checks:

If a candidate can do these three, they understand the work.
If they cannot, they are relying on remembered words or generated patterns.

Step 1: The Paraphrase Check

Ask:

Explain this problem in your own words.

Real Skill Signals:

• Clear, simple explanation

• Identifies core constraints

• Describes mental model

AI-Powered Answer Signals:

• Overly formal language

• Vague phrasing

• No constraint awareness

Reason: Real understanding compresses. AI expands.

Step 2: The Tradeoff Check

After the candidate describes their solution, ask:

What other approaches did you consider and why did you choose this one?

Real Skill Signals:

• Discusses performance, cost, reliability, complexity

• Can compare pros and cons meaningfully

AI-Powered Answer Signals:

• Provides a list of options with no clear selection criteria

• Avoids describing what they would sacrifice or optimize

High-performing engineers consistently reference tradeoffs, not just solutions.

Step 3: The Constraint Shift Check

Ask:

If one key assumption changed, how would your approach change?

Examples:

• The dataset is now streaming instead of batch.

• Latency requirement is half of what you assumed.

• Traffic is 10 times higher than expected.

Real Skill Signals:

• Candidate adapts

• Explains new failure modes

• Revises architecture or algorithm logically

AI-Powered Answer Signals:

• Repeats original solution

• Adds vague scaling language

• Changes answer without reasoning context

This is the single strongest authenticity indicator.

The Code Interview Version

Do not evaluate:

• Final code output

• Library choice

• Syntax accuracy

Evaluate:

• How they debug

• How they refactor

• How they reason about complexity

Ask:

Show me where this code could break and how you would test it.

Real engineers answer immediately.
AI-dependent candidates struggle.

The Behavioral Interview Version

Do not evaluate:

• Story polish

• Structure

• Confidence

Evaluate:

• Ownership

• Emotional recall detail

• Personal accountability

Ask:

What changed during the project and why?

Real memories always contain change.
AI-generated stories rarely do.

How Sherlock AI Helps

Sherlock AI detects:

• Background whisper coaching

• Identity inconsistency

• Scripted narrative pacing

• Copy-paste and code authorship anomalies

Sherlock AI does not decide whether the candidate is good.
It ensures:

• The person answering is the applicant

• The thinking is their own

This keeps interviews fair and high-signal.

Example Scorecard Language (Copy-Paste)

Candidate demonstrated clear reasoning when explaining solution and could articulate tradeoffs and adjustments when constraints changed. Reasoning authenticity confirmed

If concerns arise:

Candidate provided complete answers but could not explain reasoning or adapt approach. Signals suggest memorization or external assistance

This protects fairness and audit safety.

Conclusion

AI did not eliminate skill.
It eliminated lazy evaluation of skill.

To measure real capability:

• Evaluate reasoning

• Evaluate adaptability

• Evaluate decision logic

Not:

• Polished language

• Memorized frameworks

• Working code alone

This is how hiring remains accurate in the AI era.