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

Chapters

1Foundations of Real-Time Retrieval-Augmented Generation

1.1 Understanding Real-Time RAG Basics 1.2 Core Components of a RAG System 1.3 Ingestion vs Retrieval vs Generation 1.4 Latency Budgets and SLA Considerations 1.5 Gemini's Multimodal Capabilities Overview 1.6 Live API Access Patterns 1.7 Data Freshness and Staleness Handling 1.8 Embeddings and Retrieval Models 1.9 Context Windows and Overlap 1.10 Caching and Memoization Strategies 1.11 Safety, Guardrails, and Compliance Basics 1.12 Evaluation Frameworks for Real-Time RAG 1.13 System Architecture Diagramming 1.14 Failure Modes and Disaster Recovery 1.15 Tooling for Local Development

2Gemini Fundamentals: Architecture and Multimodal Capabilities

3Data Sources and Vector Stores for Real-Time RAG

4Real-Time Ingestion and Streaming Data Pipelines

5The Multimodal Live API: Authentication, Endpoints, and Workflows

6Prompt Engineering for RAG with Gemini

7Memory and Context Management in Real-Time RAG

8Latency, Throughput, and Quality of Service

9Security, Privacy, and Compliance in RAG Systems

10Evaluation, Metrics, and A/B Testing for RAG

11Deployment and Orchestration: Cloud and Edge

12Case Studies: Real-World RAG Scenarios

13Advanced Retrieval Techniques: Hybrid Search and Re-ranking

14Observability: Monitoring, Logging, and Debugging

15Extending Gemini with Custom Tools and Plugins

Courses/Building Real-Time RAG Systems with Gemini & the Multimodal Live API/Foundations of Real-Time Retrieval-Augmented Generation

Foundations of Real-Time Retrieval-Augmented Generation

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Establish the core concepts, workflows, and constraints of real-time RAG systems, and position Gemini as the enabler for multimodal live reasoning.

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7 of 15

1.7 Data Freshness and Staleness Handling

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