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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
    • The Retrieval Workflow
    • Preparing Engineering Documents
    • Keyword and Semantic Search
    • Embeddings and Vector Storage
    • Grounded Answers and Abstention
    • Specification Assistant Lab
  • 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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‹AI Skills for VLSI Engineers
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Retrieval-Augmented Generation

Retrieval-Augmented Generation · Beginner

Retrieval-Augmented Generation

Find applicable specification evidence, preserve citations, and decline unsupported answers.

  • 6topics
  • 6lessons

RAG means retrieving relevant evidence and supplying it to an answer-writing step. For RTL, DV, STA, and PD engineers, the first challenge is finding the right revision and preserving the conditions attached to a requirement. This section starts with that workflow, introduces keyword and semantic retrieval without vector mathematics, and finishes with a small inspectable keyword retriever.

Lesson

What you will practice

Retrieval workflow

Separate source selection, retrieval, answering, and verification.

Preparing documents

Keep requirements, qualifiers, versions, and source locations together.

Keyword and semantic search

Understand exact terminology, paraphrases, and retrieval misses.

Embeddings and vector storage

Understand what vectors index and when a vector database is optional.

Grounded answers

Check claim-level citations and handle missing or conflicting evidence.

Specification assistant lab

Read a standard-library retriever with version filtering and abstention.

Tip

All specification excerpts are fictional. The final lab extracts text rather than calling or imitating an LLM. No retrieval quality or model performance is claimed.

Related topics

  • Up: Working with AI

  • Prerequisite: Engineering Data

  • Start: Retrieval workflow

  • Next section: Tools, workflows and agents

PreviousNext: The Retrieval Workflow

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Up nextThe Retrieval Workflow

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See also

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