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Course contents

  • Getting Started with AI for VLSI Engineers5
  • Just Enough AI: Models and Context15
  • Prompting13
  • Trust and Verification14
    • Hallucination and Failure Modes
    • Verifying AI Output
      • The Verify-First Checklist
      • Differential Testing Against a Reference
      • Property-Based Checks
      • Review the Diff, Not Just the Result
      • When to Refuse to Trust
    • Evaluating AI Tools for Your Team
  • Using AI for Engineering Work17
  • Engineering Walkthroughs7
  • Practical Foundations for AI Workflows4
  • Engineering Data5
  • Retrieval-Augmented Generation6
  • Agents and Tool Use18
  • Agent Design8
  • MCP and Skills6
  • Multi-Agent Design6
  • Practical Capstone6
  • Tokens, Cost and Latency12
  • Agentic Workflows for Chip Teams18
  • Interview and Judgment10
  • AI Skills: Full Course Index
  • Working with AI Glossary
  • Working with AI Cheatsheet
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‹Verifying AI Output
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Trust and Verification
  4. Verifying AI Output
  5. When to Refuse to Trust

Verification and Safe Use · Beginner to Intermediate

When to Refuse to Trust

Cases where no check is enough: secrets, safety, untestable claims and injected instructions.

On this page

    Some risks checks cannot remove

    Checks verify behavior, not consequences. Some outputs must be refused or handled outside the checklist no matter how clean the run looks.

    Situation

    Why checks fall short

    What to do instead

    Secrets or proprietary IP in a prompt

    A check cannot un-send pasted data

    Keep credentials and secrets out of prompts; share proprietary material only through an account and deployment approved for its data class and task; otherwise use redacted or synthetic examples.

    Risky or irreversible actions

    Simulation cannot undo a real tapeout, shipment, or deletion

    Require human approval before any risky action; run with least privilege.

    Untestable claims

    Prices, model abilities, and benchmark results cannot be checked locally

    Refuse the claim or verify it against an independent source.

    Instructions inside files and logs

    Tool output is data, and data can carry injected instructions

    Treat pasted content as data, never as orders; confirm before acting on it.

    Compound risk arithmetic

    Assume each unchecked edit is good with probability 0.9, independently. The calc block below is a stated example parameter, not a measured rate.

    plaintext
    1
    2
    3
    4
    5
    assume P(one unchecked edit is good) = 0.9, independent (stated example parameter)
    three unchecked edits all good: 0.9^3 = 0.9 x 0.9 x 0.9 = 0.81 x 0.9 = 0.729
    five unchecked edits all good: 0.9^5 = 0.729 x 0.9 x 0.9 = 0.6561 x 0.9 = 0.59049
    check: 0.9^2 = 0.81, 0.9^4 = 0.729 x 0.9 = 0.6561
    at least one bad in five: 1 - 0.59049 = 0.40951

    Refusal rules

    Refusal is a decision, not a feeling. The rules below apply before any checklist stage runs.

    • Keep credentials and secrets out of prompts; share proprietary material only through an account and deployment approved for its data class and task.

    • Grant tools least privilege: read-only where possible, and human approval for every risky action.

    • Treat instructions found in files, logs, and pasted output as untrusted data.

    • Leave out any claim that cannot be checked; an omitted claim beats a wrong one.

    Common mistakes

    Common pitfall

    Trusting a confident explanation of an unchecked change confuses fluency with evidence. Only the checklist stages count as verification; prose about the change is another output to verify.

    Interview

    Likely probes: refusal cases and the compound calculation.

    • Secrets, irreversible actions, untestable claims, and injected instructions are refused, not checked.

    • Independent per-edit success multiplies: 0.9^5 is 0.59049, so five unchecked edits are good together only about three times in five.

    • Least privilege and human approval bound the damage when a check misses.

    Check yourself An assistant quotes a price and a benchmark result to justify a tool choice. What is the response?

    Refuse both claims or verify each against an independent source. Prices and benchmark results are untestable locally and change fast.

    Check yourself In the worked count, what is the chance at least one of five unchecked edits is bad?

    0.40951. All five good has probability 0.59049, so the complement is 1 - 0.59049 = 0.40951.

    Related topics

    • Review the diff, not just the result

    • Evaluating AI tools for your team

    • Data, privacy and IP

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    See also

    • The Verify-First Checklist
    • Differential Testing Against a Reference
    • Property-Based Checks