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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
      • Invented APIs, Flags and Signal Names
      • Plausible but Wrong Numbers
      • Overconfident Tone and Sycophancy
      • Instruction Drift in Long Sessions
      • Subtle Bugs That Compile
    • Verifying AI Output
    • 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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‹Trust and Verification
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Trust and Verification
  4. Hallucination and Failure Modes

Verification and Safe Use · Beginner to Intermediate

Hallucination and Failure Modes

How assistants fail on engineering tasks, and the checks that catch each failure.

  • 5topics
  • 5lessons

Assistant output reads fluently even when it is wrong. This topic catalogues the failure modes that matter on engineering tasks, with a detection check and an engineering example for each.

Mode

Typical sign

Detection check

Invented names

A flag, call or signal that sounds right

Look up every name in the real reference.

Wrong numbers

Confident arithmetic or a stale fact

Recompute from stated inputs and current tables.

Tone and agreement

Certainty, or agreement with a wrong premise

Test the claim against counter-evidence.

Instruction drift

Late turns drop early constraints

Rescan the session against the stated contract.

Bugs that compile

Plausible code with a hidden defect

Run boundary tests that cover the range ends.

Name, number and test gates for assistant output.
Each reply passes name, number and test gates before acceptance.

Lesson

What it covers

Invented APIs, flags and signal names

Fluent but invented names, with reference checks and a flag audit program.

Plausible but wrong numbers

Wrong arithmetic and stale facts, with recomputation and repricing checks.

Overconfident tone and sycophancy

Certainty as style and agreement with wrong premises, with counter-checks.

Instruction drift in long sessions

Fading constraints over many turns, with restatement and rescan habits.

Subtle bugs that compile

Plausible code with hidden defects, caught by boundary testbenches.

Check yourself An assistant reply names a flag that the tool reference does not list. What is the verdict?

Treat the name as invented until the reference confirms it. Fluency is not evidence that the flag exists.

Interview

Carry one check per mode: the reference for names, recomputation for numbers, counter-evidence for agreement, a rescan for drift, and boundary tests for code.

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See also

  • Verifying AI Output
  • Evaluating AI Tools for Your Team