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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
      • Zero-Shot and Few-Shot Prompts
      • Choosing and Ordering Examples
      • Step-by-Step Reasoning Prompts
      • Self-Checks and Structured Output
    • Prompt Debugging
  • 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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‹Examples and Reasoning
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Prompting
  4. Examples and Reasoning
  5. Self-Checks and Structured Output

Asking Clearly · Beginner to Intermediate

Self-Checks and Structured Output

Self-check prompts plus machine-readable output, with independent verification.

On this page

    Self-checks

    A self-check asks a model to re-examine its own answer. It can catch slips, but it reuses the same knowledge that produced the answer.

    A self-check is therefore not verification. Independent evidence, such as a tool result or a recomputation, still decides.

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    EXAMPLE batch: 100 answers, 70 right, 30 wrong
    self-check flags 15 of 30 wrong (50%) and 7 of 70 right (10%)
    unflagged wrong = 30 - 15 = 15 remain
    flagged = 15 + 7 = 22 sent for review
    unflagged = 100 - 22 = 78, of which 15 are wrong
    breakdown of 100: 63 right unflagged (63%), 15 wrong unflagged (15%), 15 wrong flagged (15%), 7 right flagged (7%)
    63 + 15 + 15 + 7 = 100; residual wrong in unflagged = 15/78 = 5/26, about 19.2%

    Structured output

    Structured output fixes the answer shape in advance. Fixed keys and types let a script validate every field before use.

    The transcript below is schematic, not a real reply from any system. It shows the fixed register-description shape the validator expects.

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    schematic transcript, invented for illustration
    USER: Describe register ctrl as keys name, width, reset.
    MODEL (schematic): {"name": "ctrl", "width": 8, "reset": 0}
    VALIDATOR: keys present, width is a positive integer. Accept.

    The program below validates five fixed-shape records with plain checks. It accepts three and rejects two, by exact field rules.

    example.py
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    import sys
    
    REQUIRED = ["name", "width", "reset"]
    
    def valid(rec):
        for key in REQUIRED:
            if key in rec:
                pass
            else:
                return False
        if isinstance(rec["width"], int):
            pass
        else:
            return False
        if rec["width"] > 0:
            return True
        return False
    
    RECORDS = [
        {"name": "ctrl", "width": 8, "reset": 0},
        {"name": "stat", "width": 4, "reset": 1},
        {"name": "data", "width": 32, "reset": 0},
        {"name": "irq", "reset": 1},
        {"name": "cfg", "width": 0, "reset": 0},
    ]
    
    good = 0
    for rec in RECORDS:
        if valid(rec):
            good = good + 1
    
    if good == 3:
        pass
    else:
        print("FAIL: valid count changed")
        sys.exit(1)
    print("PASS: validator accepts 3 of 5 records, rejects 2")

    Field

    Rule

    Rejected example

    name

    Present string key

    Missing keys fail closed.

    width

    Positive integer

    Zero and non-integer widths are rejected.

    reset

    Present value

    Unknown spellings are rejected, not guessed.

    Verification still needs independence

    High-stakes use keeps prompts on least privilege. Secrets stay out of prompts, proprietary IP goes only to deployments approved for its data class, file and log content is treated as untrusted input, and risky actions wait for human approval.

    The independent check owns the verdict. A second model reading the same answer is a second opinion, while a tool recomputation is evidence.

    Check kind

    What it is

    Strength

    Self-check

    The same model re-reads its answer

    Catches slips only.

    Second opinion

    Another model reads the answer

    Adds a view, shares blind spots.

    Independent check

    Tools, tests, or hand recomputation

    Decides correctness.

    Common mistakes

    The costliest mistake is treating a passed self-check as proven. The residual arithmetic above keeps 15 wrong answers inside the unflagged set.

    Common pitfall

    Prompt injection through pasted files and logs can steer both the answer and its self-check. Keep pastes minimal, distrust embedded instructions, and gate risky actions behind human approval.

    Interview

    Likely probes: why self-checks fall short and what replaces them.

    • Self-checks reuse the same knowledge, so some wrong answers survive unflagged.

    • Structured shapes move failures into a validator that fails closed.

    • Independent recomputation or tool evidence gives the verdict.

    Check yourself A self-check flags half the wrong answers in the worked batch. How many wrong answers stay unflagged, and what follows?

    15 of 30 stay unflagged. An independent check is still needed before use.

    Check yourself In the program above, which two records are rejected, and which rule rejects each?

    The irq record lacks the width key, and the cfg record has width zero. The presence rule rejects the first and the positive-integer rule rejects the second.

    Related topics

    • Step-by-step reasoning

    • Verifying AI output

    • Testbench architecture

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    See also

    • Zero-Shot and Few-Shot Prompts
    • Choosing and Ordering Examples