This comprehensive course teaches you how to design, implement, and operate real-time Retrieval-Augmented Generation (RA...
Establish the core concepts, workflows, and constraints of real-time RAG systems, and position Gemini as the enabler for multimodal live reasoning.
Dive into Gemini's model architecture, multimodal reasoning, and API ecosystem to understand how to harness its full potential.
Learn how to select, prepare, and index data sources, and how to use vector stores to enable fast, relevant retrieval in real time.
Build robust streaming architectures to ingest, validate, and propagate data in real time for RAG systems.
Master how to securely access Gemini's Multimodal Live API, orchestrate endpoints, and design resilient client workflows.
Design effective prompts that leverage Gemini across modalities, control flow, and tool invocations to maximize accuracy and reliability.
Explore memory architectures, context management strategies, and personalization techniques to maintain coherent, up-to-date responses.
Measure, optimize, and guarantee latency and throughput targets while balancing resource use and quality of service.
Implement robust security, data privacy, and regulatory compliance across data flows, storage, and model interactions.
Define, collect, and analyze evaluation metrics to measure RAG quality, reliability, and user impact, with rigorous experimentation practices.
Strategies for deploying RAG systems at scale, including containerization, Kubernetes, edge compute, and CI/CD pipelines.
Explore a diverse set of real-world RAG implementations to extract patterns, pitfalls, and lessons learned.
Push retrieval quality further with advanced strategies for hybrid search, reranking, and temporal relevance.
Establish end-to-end observability to monitor, debug, and improve real-time RAG pipelines.
Learn how to extend Gemini with custom tools, plugins, and safe integrations to boost capability and automation.