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

Chapters

1Foundations of Real-Time Retrieval-Augmented Generation

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

10.1 Defining Evaluation Metrics for RAG 10.2 Retrieval Quality Metrics (Recall, Precision)10.3 Answer Quality and Faithfulness 10.4 User Satisfaction and UX Metrics 10.5 A/B Testing Methodologies 10.6 Controlled Experiments and Hypotheses 10.7 Data Collection for Evaluation 10.8 Ground Truth and Benchmark Datasets 10.9 Statistical Significance and Power 10.10 Offline vs Online Evaluation 10.11 Error Analysis and Debugging 10.12 Toxicity and Safety Evaluation 10.13 Fairness and Bias Audits 10.14 Reproducibility and Auditing 10.15 Continuous Monitoring of Metrics

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

Evaluation, Metrics, and A/B Testing for RAG

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Define, collect, and analyze evaluation metrics to measure RAG quality, reliability, and user impact, with rigorous experimentation practices.

Content

13 of 15

10.13 Fairness and Bias Audits

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