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Module 1 of 7 · 48 min

Navigate the Gemini Ecosystem and Interfaces

Distinguish Gemini Apps, Google AI Studio, the Gemini API, models, SDKs, and hosted capabilities so each task begins on the right surface with current evidence.

Google

By the end

You will be able to

  • Distinguish Gemini Apps, Google AI Studio, the Gemini API, models, SDKs, and application code.
  • Choose a surface by user, repeatability, integration, data, authority, and evidence needs.
  • Verify current model, feature, account, region, and lifecycle claims from official Google sources.
  • Move a prototype toward code without copying credentials, hidden state, or untested assumptions.
01

Separate products, interfaces, and models

Gemini is used across several Google products and developer surfaces. Gemini Apps provide end-user conversational experiences. Google AI Studio supports prompt and capability experimentation and can produce starter code. The Gemini API and Google Gen AI SDKs let an application send requests under code-controlled contracts.

A product, interface, API, SDK, and model are different layers. A feature visible in a Gemini App is not automatically an API capability, and an AI Studio experiment is not automatically production code. Record the exact surface, account context, model identifier, configuration, and date when behavior matters.

02

Choose the surface by outcome

Use a Gemini App for interactive end-user work where its product controls meet the task. Use Google AI Studio to explore prompts, settings, and supported capabilities. Use the API when software needs repeatable inputs, structured handling, application-owned validation, integration, or operational evidence.

Choose from the learner's outcome, data classification, audience, accessibility, authentication, connected data, tool authority, repeatability, scale, latency, cost, audit, and recovery requirements—not from a feature checklist alone.

03

Treat AI Studio as a prototype boundary

Google AI Studio can run conversational prompts, expose run settings, enable supported tools, and generate starter code. It is useful for learning a request shape and comparing prompt versions, but saved prompts and generated snippets still require review.

Before moving to an application, extract system instructions, messages, model and generation settings, tools, safety settings, expected output, and evaluation cases. Replace embedded keys with an environment or managed-secret boundary and verify error, privacy, retention, and retry behavior.

04

Treat model choice as configuration

Gemini model families, aliases, previews, capabilities, limits, and lifecycle states change. Select a model for measured quality, modalities, tool support, latency, cost, availability, and risk, then keep the identifier and model-specific settings in reviewed configuration.

Verify current status from the official model catalog and the documentation for the exact API surface. Preview or experimental labels require stronger change monitoring and rollback; a consumer-product model name should not be assumed to equal an API model identifier.

05

Start with a reproducible record

For every experiment, record the outcome, surface, account type, model reference, prompt version, inputs, settings, tools, date, output, validation result, latency or usage when available, and source links. Exclude credentials and sensitive content from shared evidence.

When no Gemini account or paid call is available, learn the same decision skill from official screenshots, documented request fixtures, and a surface-selection worksheet. The objective is a defensible interface contract, not consumption.

Practice activity

Choose and document a Gemini surface

  1. Compare Gemini Apps, Google AI Studio, and the Gemini API for three scenarios: an individual explanation, a team prompt prototype, and a repeatable application workflow.
  2. For each scenario, document user, data, account, integration, authority, repeatability, accessibility, cost, audit, and recovery requirements.
  3. Use current official sources to record the chosen surface and model-selection method without hard-coding a model name.
  4. Run one approved no-cost experiment if access permits, or use the supplied conceptual request/response fixture; preserve a secret-free reproducibility record and decision.

What to produce

  • A three-scenario surface decision matrix with dated official sources, rejected alternatives, assumptions, and residual risks.
  • A secret-free experiment or conceptual fixture record containing surface, model configuration method, prompt version, settings, output contract, validation result, and next step.

Reflect before continuing

Which requirement changed your choice from a Gemini App or AI Studio prototype to an API application—or in the other direction?

Evidence

Sources and verification

Knowledge check

Make it stick.

Pass at 80%

Choose the strongest answer for each question. Your attempts become part of your device-local transcript.

01Which statement is most accurate?
02When is the Gemini API usually the strongest fit?
03What should happen before AI Studio starter code enters an application?
04How should a reusable project select a Gemini model?
05How can a learner complete the surface-selection objective without a paid call?