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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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‹AI Skills for VLSI Engineers
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Practical Capstone

Practical Capstone · Beginner

Practical Capstone

Build and evaluate a small evidence assistant without requiring accounts, network access or paid models.

  • 6topics
  • 6lessons

Build a read-only assistant that turns a short synthetic RTL, DV, STA or PD packet into an evidence note for a human engineer. The core route uses the inline fixtures, a written contract and manual or scripted response replay. It requires no network, API credentials, model, simulator, MCP connection or multi-agent setup. This is an engineering workflow exercise, not an autonomous signoff system.

Complete the lessons in order. Practice on K1-K3, freeze your workflow, then attempt H1-H3 before opening their answer. Keep answers out of the assistant's packet. The supplied fixtures and responses are fabricated teaching material; no simulation or model call is claimed. Optional integrations come only after the offline route works and must use approved inputs and separately recorded actual results.

Lesson

What you deliver

Define the Assistant

Scope an offline evidence-triage assistant for synthetic engineering packets.

Assemble the Workflow

Connect input validation, a guided request/response contract and human review.

Troubleshoot the Workflow

Diagnose input, transport, schema and evidence failures separately.

Evaluate Known and Unseen Cases

Freeze the workflow and assess held-out cases with an explicit rubric.

Compare Effort and Cost

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

Final Review and Handover

Deliver a reproducible evidence package with clear operational limits.

  • Final artifact: input manifest, contract, response notes, validation record, score sheet, effort ledger and handover.

  • Acceptance: traceable claims, correct uncertainty, enforced scope and independent human checking.

  • Model or team quality is measured only from actual evaluated runs, never from fabricated response replay.

Related topics

  • Up: Working with AI

  • Previous: Multi-Agent Design

  • Start: Define the Assistant

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See also

  • Getting Started with AI for VLSI Engineers
  • Just Enough AI: Models and Context
  • Prompting