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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
    • Verifying AI Output
    • Evaluating AI Tools for Your Team
      • Define the Task Before Measuring Anything
      • Build a Small Labelled Test Set
      • Measure Pass Rate and Time Saved
      • Track Cost per Accepted Result and Decide
  • 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. Evaluating AI Tools for Your Team

Verification and Safe Use · Beginner to Intermediate

Evaluating AI Tools for Your Team

A small test set, a fair baseline, and cost per accepted result for judging AI tools.

  • 4topics
  • 4lessons

Teams choose tools from demos and anecdotes, which hide the miss rate. A small labelled test set plus a fair baseline supports task-specific comparison of pass rate, time, and cost per accepted result. The fixed outcomes, times, and costs in these lessons are synthetic fixtures for demonstrating arithmetic, not measurements of any tool or engineer.

Claim

Measurement that tests it

The tool does the task well

Pass rate on a frozen labelled set with a written done rule.

The tool saves time

Paired same-task comparison, balanced run order, same done rule, all review and fix time counted.

The tool is worth its spend

Measured tool spend per accepted result, alongside human effort and quality.

Evaluation loop from task definition to decision and re-check.
An evaluation loop: fixed tasks, measured results, then a decision with a re-check date.

Lesson

What it covers

Define the task

Scope, inputs, outputs, and the written done rule.

Build a test set

Sampling real work, labelling answers, and freezing the set.

Measure rate and time

A fixed harness, pass rate with its range, and fair time comparison.

Track cost and decide

Cost per accepted result, side-by-side options, and the re-check.

Check yourself Why must the pass rule be written before running any tool?

Otherwise the rule bends to fit the outputs already seen. A frozen rule keeps every option on the same test.

Interview

Carry one sentence per lesson: the done rule decides pass before measurement, the set is sampled and frozen, the harness reports rate with range and time against a fair baseline, and spend divided by accepted results drives the decision.

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See also

  • Hallucination and Failure Modes
  • Verifying AI Output