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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
  • 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
    • Define the Assistant
    • Assemble the Workflow
    • Troubleshoot the Workflow
    • Evaluate Known and Unseen Cases
    • Compare Effort and Cost
    • Final Review and Handover
  • 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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‹Practical Capstone
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Practical Capstone
  4. Compare Effort and Cost

Practical Capstone · Beginner

Compare Effort and Cost

Account for review, retries, setup and actual usage against a manual baseline.

On this page

    Problem: the cheapest draft can cost the most to review

    A short answer that needs ten minutes of correction may be more expensive than a careful manual note. Compare complete workflows, including setup, reading, validation, human correction, retries and handover. Measure quality at the same acceptance gate before comparing cost. A cheaper route that invents evidence or misses a supported issue has not completed the same job. Keep human effort, elapsed time and billed usage separate because they answer different questions.

    Use the same frozen packets and reporting format for the manual baseline and assistant route. If one engineer performs both, alternate the order across fresh cases and disclose the memory effect. Record start and finish times, active human minutes, every attempted request, and any usage the approved adapter actually reports. When there is no model in the offline route, write 'no model usage measured.' The replay route can teach cost accounting but cannot estimate a real model's accuracy or throughput.

    Worked synthetic ledger: arithmetic only

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    Invented totals for six cases; not observed performance:
    manual: setup 0 min + reading/writing 36 min + review 6 min = 42 min
    offline assisted replay: setup 20 min + operation 6 min
      + review/correction 18 min + handover 4 min = 48 min
    replay model calls: none; model quality/cost: not measured
    
    Optional fictional adapter usage, separate arithmetic example:
    all attempts combined: input=18000 tokens, output=3000 tokens
    fictional rates: 2 units / million input, 6 units / million output
    usage cost = 18000*2/1000000 + 3000*6/1000000 = 0.054 units
    These rates are not a product quote. No model performance is implied.

    The ledger shows why parallel speed and total effort must not be conflated. A team may finish draft work sooner while using more calls and more reconciliation time. Conversely, a reusable local checker may have a large setup cost and a low recurring cost. For a fair reuse estimate, state which setup work is reusable and which review work recurs. Do not omit failed calls or expensive cases just because the final answer eventually passed.

    Exercise: compare without hiding setup

    Assume the invented six-case workload repeats without changes. The manual route remains 42 human minutes per batch. The replay route pays the 20-minute setup once and 28 recurring minutes per batch. Calculate total human effort after one batch and after three batches. Decide what additional evidence is required before choosing replay, one real assistant or an optional multi-agent team. Finally state whether the fictional usage cost can be described as a current provider price.

    Check yourself What do these numbers support?

    One batch: manual 42 minutes, replay 48. Three batches: manual 126, replay 20+3*28=104. Replay becomes lower in this invented arithmetic from the second unchanged batch: 84 versus 76. This assumes reusable setup and equal quality, neither established by the numbers. Use actual accepted-case scores, review effort, elapsed time and all charged attempts before selecting a real assistant or team. The 0.054 units are a fictional rate exercise, not a quote or measured spend.

    Quality reporting should include supported findings missed, false alarms, unsupported certainty, incomplete cases and unauthorized-action attempts. A single average can hide a severe failure on one packet type. If the team uses a different model or larger total budget, disclose that confound rather than attributing a change to teamwork. Record unknown usage explicitly; do not silently substitute zero. Actual invoices or adapter usage logs are stronger cost evidence than token counts guessed from text length.

    Limits and source

    A small controlled comparison guides the next experiment rather than proving a permanent winner. Different packet lengths and review standards can change the result. NIST AI RMF 1.0, January 2023 is background for treating evaluation as ongoing work. This lesson deliberately avoids current product prices; an optional real deployment needs its applicable rate sheet, version and measurement date recorded separately.

    Related topics

    • Up: Practical Capstone

    • Next: Final Review and Handover

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    See also

    • Define the Assistant
    • Assemble the Workflow
    • Troubleshoot the Workflow