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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
    • APIs: Requests, Responses and Errors
    • JSON Records and Meaningful Validation
    • Repositories, Diffs and Isolated Changes
    • Working with Waveforms and Report Images
  • 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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‹Practical Foundations for AI Workflows
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Practical Foundations for AI Workflows
  4. APIs: Requests, Responses and Errors

Practical foundations · Beginner

APIs: Requests, Responses and Errors

A guided introduction for VLSI engineers: APIs: Requests, Responses and Errors.

On this page

    You already use interfaces in hardware: an initiator supplies a request, a target returns a response, and both follow a contract. An API is a software interface with a similar need for a defined contract. It is not an AXI transaction: transport, timing, retries and error meanings are different. For this course, start with a request for a synthetic regression summary, not a model-provider SDK.

    Follow one request

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    Illustrative exchange, not a real service call
    GET /runs/run-014
    request: retrieve one approved run record
    response status: 200
    response body: {"run_id":"run-014","test":"fifo_wrap","status":"FAIL","revision":"r3"}
    next action: validate the body, then include the record as evidence

    Concept

    What it means

    What to check

    Endpoint and method

    Where the request goes and which operation it asks for.

    Use an approved endpoint and supported method; a read-looking name is not proof of no side effects.

    Arguments and body

    The run ID, query or other structured input.

    Validate allowed values and minimise the data sent.

    Authentication

    Evidence of the caller's identity.

    Use the application's approved credential mechanism; never paste credentials into a prompt or lesson output.

    Authorisation

    Which operation and data that caller may access.

    Authentication does not grant access to every project.

    Response

    Status plus returned data.

    A successful transport response does not establish that an AI explanation is correct.

    An error is information, not permission to keep trying

    An HTTP 401 indicates missing or invalid authentication; a 403 means the request is forbidden. Do not ask the assistant to bypass either. A 429 indicates a rate limit; follow the service's retry guidance and the run budget. A timeout leaves the outcome uncertain, particularly for a job submission: retrying can launch a second job. Use approved deduplication or ask the owner to reconcile state. A finite step budget alone cannot stop one tool call that hangs forever; the actual executor also needs request and job timeouts.

    Exercise

    Your log assistant receives 200 with the wrong run_id, 403 for another project's report, and a timeout after submitting a simulation. For each, decide whether to accept, retry or escalate, and name the evidence you need.

    Check yourself What are the three decisions?

    Reject the mismatched record and investigate request/response correlation. Respect the 403 and ask for approved access if necessary. Reconcile the simulation's status before any retry; a timeout does not prove it was never started.

    Optional reading: HTTP semantics defines status codes and method semantics. Model APIs add their own schemas, limits and billing. Consult the chosen service's current documentation rather than transferring the synthetic response shape to it.

    Related topics

    • Up: Practical foundations

    • Next: JSON Records and Meaningful Validation

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    See also

    • Repositories, Diffs and Isolated Changes
    • Working with Waveforms and Report Images