The outcome

Select and integrate a pinned model using evidence from a reproducible evaluation rather than a playground impression.

Vertex AI is a Google Cloud platform for generative AI models, agents, retrieval, tuning, evaluation, and ML operations. Building and operating production generative AI and ML systems with Gemini and Model Garden models, grounding, RAG, evaluation, tuning, agents, and Google Cloud controls. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Define the use case, data classification, region, model eligibility, output schema, evaluation set, quality thresholds, latency, and budget.

  2. 02

    Create an isolated Google Cloud project and service account with least-privilege Vertex AI permissions, quotas, billing alerts, and approved networking.

  3. 03

    Call candidate models through the current Gen AI SDK with pinned IDs, explicit system rules, bounded generation settings, and non-sensitive fixtures.

  4. 04

    Evaluate factuality, grounding, safety, multilingual behavior, structured output, long context, streaming, errors, latency, and token use consistently.

  5. 05

    Record model and configuration versions, deploy behind application validation and monitoring, and require regression review before any model change.

Working standard

What good use looks like.

  • Use one benchmark across models.
  • Pin model and generation settings.
  • Set quotas and billing alerts early.

Vertex AI capabilities, models, retention conditions, and data locations vary by feature and region. Use isolated projects and service accounts, protect credentials, confirm data governance and zero-retention conditions for the exact feature, enforce quotas and billing alerts, preserve source permissions in retrieval, evaluate safety and grounding, and regression-test every model, prompt, index, and SDK change.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.