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Course contents

  • Getting Started with AI for VLSI Engineers5
  • Just Enough AI: Models and Context15
  • Prompting13
    • Anatomy of a Prompt
    • Examples and Reasoning
    • Prompt Debugging
      • Diagnose the Failure First
      • Change One Thing at a Time
      • Keep a Small Set of Test Prompts
      • A Prompt Regression Harness
  • Trust and Verification14
  • 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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‹Prompt Debugging
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Prompting
  4. Prompt Debugging
  5. Change One Thing at a Time

Asking Clearly · Beginner to Intermediate

Change One Thing at a Time

Change one prompt element per version, rerun the full set, and log before-and-after counts.

On this page

    One variable per edit

    Edits that change several elements at once hide their own cause. When the set improves, no one knows which edit did it.

    One element per version keeps the cause visible. The log records the version, the single change, and the set score.

    The edit loop

    Each version follows the same four steps. The set from the next lesson supplies the score.

    Step

    Action

    Record

    Freeze

    Copy the current prompt as a numbered version.

    Version number and full text.

    Change

    Alter exactly one element: a fact, a definition, or the shape.

    Which element changed and why.

    Rerun

    Run every test prompt, not just the failing one.

    Passing count over total count.

    Keep or revert

    Keep versions that raise the score without breaking others.

    New score beside the old score.

    A worked loop

    One version pins the output shape and the set moves from 2 of 6 to 3 of 6. Repeated runs then check the gain is stable.

    Sampling rescales with temperature, and temperature 0 is approximately greedy. That is not a guarantee of identical output across runs, so rerun even at temperature 0.

    plaintext
    1
    2
    3
    4
    agreement across 5 runs of one version (example counts):
    identical replies: 4 -> 4 / 5 = 80%
    set score after the edit: 3 of 6 hold, up from 2 of 6
    temperature 0 is approximately greedy, not identical: rerun before judging an edit

    Common mistakes

    Common pitfall

    Rewriting the whole prompt between runs. Five simultaneous edits give five suspects; change one element and rerun the set.

    Interview

    Likely probes: why single-variable edits win and what the log holds.

    • One change per version keeps the cause of any score move visible.

    • Every version reruns the full set, since fixes can break passing cases.

    • The log holds version text, the single change, and both scores.

    Check yourself Version 5 adds two facts and a format example at once, and the set improves. What can be concluded?

    Little. Two edits confound the cause, so each edit needs its own rerun.

    Check yourself Five runs of one version at temperature 0 give 4 identical replies. What does that show?

    Agreement of 4 of 5, or 80 percent. Temperature 0 is approximately greedy rather than identical, so rerun before judging an edit.

    Related topics

    • Diagnose the Failure First

    • Keep a Small Set of Test Prompts

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    See also

    • A Prompt Regression Harness