add-gemini-feature

Integrating Gemini models for AI feature development requires a structured workflow that covers backend service implementation, API route creation, and frontend component development to handle both streaming and non-streaming AI responses.

Adding an end-to-end AI feature to StudyCanvas involves following a step-by-step process that includes backend model definition, service implementation, route creation, and frontend API integration to seamlessly display AI-generated content.

Handling streaming and non-streaming AI responses requires specific backend service implementation and frontend component development to properly receive, process, and display the continuous or singular model outputs.

Yes, you can use this structured workflow to add features like flashcard generation from selected text by guiding you through creating the necessary backend code, defining API endpoints, and building the UI component to display the flashcards.

When developing AI features, prompt engineering and model selection are essential requirements addressed during the workflow to ensure the Gemini models accurately process inputs and generate the desired text or media outputs.

Implementing frontend components for non-streaming AI responses requires careful API integration to ensure the complete model output is properly received and rendered without timing out or failing to display the final payload.