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The Google Gemini connector lets applications you build with AIWA use Gemini models for AI-powered features. You can use Gemini inside your application for experiences such as:
  • AI assistants
  • Content generation
  • Summarization
  • Classification
  • Data extraction
  • Document analysis
  • Multimodal workflows
  • Customer support
  • AI-powered automation
  • Structured generation
Once Gemini is connected, tell AIWA what the AI feature should do inside your product. Connect Gemini. Define the experience. AIWA builds the workflow around it.

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AIWA vs Gemini

AIWA is the product-building environment. Gemini is an AI provider your application can use as part of a feature. For example:
Build a customer insights dashboard with contacts, support history, recent activity, and account health.
AIWA builds the application. Then:
Add an AI Summary button that analyzes the customer’s recent activity and generates a concise account summary using Gemini.
Gemini powers the AI step inside the product.

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Before You Start

You’ll need access to the Google AI service and the credentials required by AIWA’s Gemini connector. API usage may incur charges according to your Google account, selected model, and request volume.
Model availability, pricing, rate limits, and capabilities are controlled by Google and may change over time.

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Connect Gemini to AIWA

Open your AIWA project and navigate to Connectors. Find Google Gemini and select it. Aiwa Google Gemini Follow the setup flow and provide the information requested by AIWA. Aiwa Google Gemini Config Once connected, Gemini becomes available for AI-powered features inside your application.
Keep your Google AI credentials private. Configure them through AIWA’s connector experience rather than exposing them in frontend code or public repositories.

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Build Your First Gemini Feature

Suppose you’re building an e-commerce management tool. You could tell AIWA:
Add an AI Product Assistant using Gemini. Users should provide a product name, features, audience, and tone. Generate a product description, five selling points, and three social media captions.
The workflow might look like:
The user interacts with your product, not directly with the underlying model provider.

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Define the Input

Tell AIWA what context the model needs. For example:
Use the product name, category, features, target audience, price point, and brand tone.
Or:
Use the customer’s latest enquiry and previous support messages.
Or:
Use the document text and the user’s selected analysis goal.
The feature should provide Gemini only with the information relevant to the task.

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Define the Output

Be clear about the result you want. For example:
Return a 100-word product description, five bullet-point benefits, and three short social posts.
Or:
Return category, sentiment, urgency, and recommended next action.
Or:
Return an executive summary, key decisions, important dates, and action items.
This makes it easier for AIWA to build a structured experience around the response.

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Example: AI Content Assistant

Suppose you’re building a marketing platform. You could ask:
Add a Content Assistant using Gemini. Users should enter their business, offer, audience, platform, and tone. Generate five content ideas and let users expand any idea into a complete post.
Now the experience becomes:
That’s a complete AI workflow rather than a single generation button.

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Example: Customer Support

For a support application:
Add an AI Suggested Reply button using Gemini. Use the current ticket and recent conversation context to draft a concise response. Show the draft to the support agent for review before sending.
This keeps a human in control of customer communication.

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Example: Structured Lead Analysis

For a CRM:
When a new lead submits an enquiry, use Gemini to extract their likely service requirement, budget, urgency, industry, and purchase intent. Save the structured result to the lead record.
Conceptually:
The AI result becomes usable application data.

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Example: Document Analysis

Gemini can also be used in document-oriented workflows. For example:
Add a document analysis feature using Gemini. Return an executive summary, key themes, important dates, and action items.
You can then expand the feature:
Let users ask follow-up questions about the analyzed document.
That turns one-time analysis into a more interactive experience.

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Multimodal Use Cases

Depending on the Gemini model and API capabilities available to your account, some Gemini workflows may be able to work with more than text. That can make Gemini useful for product experiences involving combinations of content types. For example, an application might analyze:
  • Text
  • Images
  • Documents
  • Other supported inputs
Available multimodal capabilities depend on the Gemini model you’re using. Check Google’s current model documentation before designing a feature around a specific input type.

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Human Review

For important actions, consider requiring a person to review AI output before anything external happens. For example:
Use Gemini to generate a proposal draft, but require the account manager to review and approve it before sending.
Conceptually:
This is especially useful for:
  • Customer communication
  • Financial decisions
  • Business recommendations
  • Sensitive content
  • High-impact workflows

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Save or Regenerate?

Decide whether AI output should become permanent application data. For example:
Save approved AI summaries to the customer record.
Or:
Don’t save the result until the user clicks Save.
Or:
Use the existing stored summary unless the user explicitly clicks Regenerate.
This can improve both user experience and API efficiency.

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Model Selection

Google may offer multiple Gemini models with different strengths. Your choice may depend on:
  • Speed
  • Cost
  • Reasoning quality
  • Context requirements
  • Supported input types
  • Output requirements
Instead of automatically choosing the most powerful option, choose based on the product need. For example:
This feature performs thousands of short classifications, so prioritize speed and cost efficiency.
Or:
This feature performs complex document analysis, so prioritize quality and deeper reasoning.

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Handle Loading and Errors

AI generation can take time or fail. Your application should make both states clear. For loading:
Analyzing…
For failure:
You can tell AIWA:
Preserve the user’s original input if the Gemini request fails and allow them to retry without starting over.

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Avoid Unnecessary Requests

Don’t call Gemini repeatedly when the existing result is still valid. For example:
Don’t regenerate the customer summary every time the page loads. Store the result and only regenerate when the user requests it or the underlying customer data changes significantly.
This can reduce latency and unnecessary API usage.

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Sensitive Data

Before sending application information to Gemini, consider whether the feature actually requires it. Avoid sending:
  • Passwords
  • API keys
  • Authentication secrets
  • Private credentials
  • Unnecessary sensitive information
Never include secret credentials as Gemini prompt context.
For applications handling sensitive business or customer information, make sure your use of the external model provider fits your privacy and compliance requirements.

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Common Problems

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Gemini feature doesn’t respond

Check whether:
  • The connector is configured correctly
  • The server request is being made
  • Google returns an error
  • The application receives the result
  • The interface displays it correctly
Use Debug Mode with the specific behavior you observe.

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Output isn’t useful

The problem may be the prompt or context rather than the connector. For example:
The generated summaries are too generic. Improve the Gemini prompt so every summary includes the customer’s problem, urgency, budget, objections, and recommended next step.

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Output structure is inconsistent

If your application expects structured information, make that explicit. For example:
Ensure Gemini always returns category, urgency, sentiment, and recommendedAction in a predictable structured format. Validate the response before saving it.

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Responses are slow

Ask AIWA to inspect:
  • Model choice
  • Input size
  • Output length
  • Number of calls
  • Application workflow

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API usage is unexpectedly high

Check whether the feature is triggering unnecessarily. For example:
Audit the Gemini workflow and make sure customer summaries aren’t regenerated every time the page loads.

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Debugging Gemini

A good Debug Mode prompt could be:
Expected: Clicking Generate Insights should analyze the customer record and display a summary. Actual: The loading state appears, but no result is displayed. The customer data itself loads correctly. Error: [PASTE ERROR IF AVAILABLE] Debug the Gemini request and response flow without changing the existing customer page.
Don’t include the API key.

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Test Your Gemini Feature

Before publishing, test:
  • Normal inputs
  • Short inputs
  • Long inputs
  • Missing input
  • Loading states
  • Failed generation
  • Retry behavior
  • Structured output
  • Saved results
  • Regeneration
  • Permissions
  • Unexpected user input
  • API usage behavior
If you’re relying on multimodal capabilities, test each supported input type separately.

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A Good Gemini Prompt for AIWA

Define: Purpose What should Gemini accomplish? Input What context does it receive? Output What should it return? User experience How does the user interact with the result? Persistence Should the result be stored? Permissions Who can use it? For example:
Add an AI Customer Insight feature using Gemini. Authorized account managers can click Generate Insight from a customer profile. Use the customer’s profile, recent activity, support history, and active deals to generate a concise summary containing account health, key risks, opportunities, and recommended next actions. Display the result in structured sections and save the approved insight to the customer record.

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Choosing an AI Provider

You don’t need every AI provider in every application. You might choose between:
  • OpenAI
  • Anthropic
  • Google Gemini
  • xAI
based on your preferred models, capabilities, cost, response characteristics, and the product experience you’re building. AIWA gives you flexibility to build around the provider that fits your use case.

xAI (Grok)

Connect xAI and use Grok inside your AIWA application.