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Course contents

  • Getting Started with AI for VLSI Engineers5
  • Just Enough AI: Models and Context15
  • Prompting13
  • Trust and Verification14
  • Using AI for Engineering Work17
    • RTL and Testbench Assistance
    • Debugging with AI
      • Log Slices That Help
      • Ranked Hypotheses with Evidence
      • Summarising and Clustering Logs
      • The Engineer Runs the Experiment
    • Scripts and Automation
    • Data, Privacy and IP
  • 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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‹Debugging with AI
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Using AI for Engineering Work
  4. Debugging with AI
  5. Ranked Hypotheses with Evidence

Everyday Engineering Assistance · Beginner to Intermediate

Ranked Hypotheses with Evidence

Ask for ranked failure hypotheses, each paired with the evidence that would confirm it.

On this page

    Hypotheses before conclusions

    A good debug reply lists guesses in rank order, not one answer. Each guess ships with the evidence that would confirm it and the observation that would rule it out.

    Confident wording is not evidence. Rank is an investigation order. Check quotations against the original log, revision, and observation conventions; existing evidence may settle a factual question without a fresh run.

    How to ask

    The request names the shape of the answer. Vague asks return vague causes, while a shaped ask returns testable pairs.

    Request element

    Example wording

    Why it is there

    Ranked list

    List three hypotheses in rank order

    Forces comparison instead of one premature answer.

    Evidence each

    Cite relevant source lines and distinguish missing observations

    Makes each claim inspectable.

    Rule-out each

    State what observation would weaken or rule each out

    Separates competing explanations.

    Next check

    Name a source inspection or focused experiment

    Bounds the next step before any edit.

    Use the same request ID, time window, and configuration when comparing explanations. A consumer stall for an unrelated transaction does not support a claim about this request. Ask which observation distinguishes a missing acceptance from a genuinely blocked consumer.

    A worked round

    The block below is a schematic transcript, illustrative only and not a reply from any real system. It shows the shaped ask and the shaped answer side by side.

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    schematic transcript (illustrative; not a reply from any real system):
    engineer: <slice: first TIMEOUT plus 20 lines before, 10 after> + "list ranked hypotheses; for each, state the evidence that would confirm it and what would rule it out"
    assistant (schematic):
    H1 consumer blocked this accepted request: inspect matching stall/completion
    events and any documented shared-resource dependency.
    H2 checker deadline conflicts with intended behavior: compare configured
    deadline and units with the approved latency requirement.
    H3 presentation was mistaken for acceptance: inspect the acceptance monitor.
    A matching completion weakens H1; a matching acceptance weakens H3.
    Missing lines remain missing evidence, not proof an event never occurred.
    plaintext
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    SYNTHETIC follow-up evidence, same R42 artifact:
    cycle 100 monitor: accepted req 4821
    cycle 101 consumer: stalled req 9000
    cycle 105 monitor: completed req 4821
    cycle 110 checker: TIMEOUT req 4821
    Exercise: does the stall line establish that req 4821 was blocked?
    Expected: no; it names a different request. Completion of 4821 suggests
    investigating checker matching, timestamp semantics, and observation paths.
    It does not alone prove the checker is the root cause.

    Common mistakes

    Common pitfall

    Treating the top-ranked explanation as a verdict skips evidence review. Checked source lines can support or contradict it, but a suggestive correlation is not causal proof. Plan a focused experiment when the existing evidence cannot distinguish the alternatives.

    Interview

    Likely probes: why rank, and what evidence means.

    • Ranking keeps alternatives visible while evidence is checked.

    • A quote is useful evidence when verified against the original artifact and relevant context.

    • Rule-outs matter as much as confirmations when guesses share symptoms.

    Check yourself A reply ranks H1 first with three supporting lines quoted. What should you check?

    Verify the quotes in the original artifact, matching run, request, units, and surrounding events. They may establish a fact or rule out a claim. Decide whether they support the causal explanation; a fresh experiment is needed only if relevant uncertainty remains.

    Check yourself The consumer stalls request 9000 while the timeout concerns 4821. What is the next useful question?

    Ask for matching events for 4821 and any documented shared-resource dependency. A stall on another request alone does not establish the timeout cause.

    Related topics

    • Summarising and clustering logs

    • Verifying AI output

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    See also

    • The Engineer Runs the Experiment