Google Gemini API Developmentin Punggol

Module 1: Introduction to Google Gemini API

• Overview of Google Gemini and Generative AI
• Understanding Large Language Models (LLMs)
• Gemini API capabilities and supported use cases
• Applications of Gemini in modern software development
• Introduction to Google Gemini API Development training in Punggol, Singapore

Module 2: Development Environment Setup

• Setting up the development environment
• Installing Python or configuring JavaScript tools
• Introduction to Google AI Studio
• Generating and configuring Gemini API keys
• Understanding API access, quotas, and usage limits

Module 3: API Fundamentals

• Understanding REST APIs and HTTP communication
• Working with JSON requests and responses
• API endpoints and request parameters
• Handling HTTP status codes and API errors
• Testing API requests using suitable development tools

Module 4: Connecting Applications to Gemini API

• Installing the appropriate Gemini SDK
• Connecting applications to Gemini models
• Sending prompts and receiving responses
• Configuring model parameters
• Managing API credentials securely

Module 5: Prompt Engineering Fundamentals

• Understanding prompt structure and instructions
• Writing clear and effective prompts
• Using context and examples in prompts
• Improving response relevance and consistency
• Testing and refining prompts for different tasks

Module 6: Text Generation and Content Processing

• Generating text using Gemini models
• Summarising documents and long-form content
• Extracting information from text
• Performing classification and text transformation
• Building content assistance applications

Module 7: Conversational AI Development

• Designing conversational applications
• Managing multi-turn conversations
• Maintaining conversation history and context
• Creating chatbot response workflows
• Implementing basic conversation error handling

Module 8: Multimodal AI Applications

• Introduction to multimodal model capabilities
• Processing supported image inputs
• Understanding supported audio and video use cases
• Combining text with other supported input types
• Developing practical multimodal application scenarios

Module 9: Structured Outputs and Data Extraction

• Understanding structured AI responses
• Generating JSON-formatted outputs
• Extracting data from unstructured text
• Defining response schemas where supported
• Validating outputs before application use

Module 10: Function Calling and Tool Integration

• Understanding function calling concepts
• Defining tools and function declarations
• Connecting model responses to application functions
• Processing tool results and returning responses
• Implementing tool execution and validation safeguards

Module 11: Embeddings and Semantic Search

• Introduction to embeddings and vector representations
• Understanding semantic similarity
• Exploring vector search concepts
• Connecting retrieval workflows with AI applications
• Introduction to Retrieval-Augmented Generation (RAG)

Module 12: Application Integration

• Integrating Gemini API into web applications
• Connecting AI features with backend services
• Processing user input and displaying responses
• Working with databases and external services
• Designing reusable application components

Module 13: Security and Responsible AI

• Secure API key storage and management
• Understanding authentication and access controls
• Handling sensitive information responsibly
• Applying input validation and output checks
• Understanding safety filters and responsible AI practices

Module 14: Testing, Troubleshooting, and Optimisation

• Testing API integrations and application workflows
• Handling errors, timeouts, and rate limits
• Understanding token usage and API costs
• Evaluating response quality and consistency
• Improving performance, reliability, and maintainability

Module 15: Deployment and Capstone Project

• Preparing an application for deployment
• Configuring environment variables and secrets
• Testing the complete application workflow
• Building a Gemini-powered chatbot or content assistant
• Demonstrating the project and reviewing implementation outcomes
• Identifying opportunities for future improvements