Building Real-Time RAG Systems with Gemini & the Multimodal Live API

What you'll learn

• Design real-time RAG architectures that combine streaming ingestion, retrieval, and multimodal reasoning
• Implement end-to-end pipelines using Gemini's Multimodal Live API and vector stores
• Engineer prompts and tool chains for multimodal, context-aware generation
• Build low-latency ingestion and streaming data pipelines for fresh context
• Manage memory and context windows to maintain accuracy and relevance in real time
• Optimize latency, throughput, and quality-of-service for production deployments
• Apply security, privacy, and governance best practices in RAG systems
• Evaluate models and pipelines with metrics, A/B testing, and observability
• Deploy and orchestrate RAG systems across cloud and edge environments
• Extend Gemini with custom tools, plugins, and advanced retrieval/re-ranking techniques

Prerequisites

Working knowledge of Python, basic ML concepts, familiarity with APIs/HTTP and cloud or container environments; prior experience with vector databases or RAG is recommended.

Skills you'll gain

• Real-time systems design
• RAG architecture
• Multimodal reasoning
• Vector search and retrieval
• Prompt engineering
• Streaming data pipelines
• Memory and context management
• Observability and monitoring
• Security and compliance
• Deployment and orchestration