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Demonstrates building retrieval-augmented generation workflows with Amazon OpenSearch Service as the vector store, including embedding ingestion, hybrid search, and integration with foundation models for grounded responses.

In this course, you will explore two techniques to improve the performance of a foundation model (FM): Retrieval Augmented Generation (RAG) and fine-tuning. You will learn about Amazon Web Services (AWS) services that help store embeddings with vector databases, the role of agents in multi-step tasks, define methods for fine-tuning an FM, how to prepare data for fine-tuning, and more. Course level: Fundamental. Duration: 1 hour. This course includes interactive elements, text instruction, and illustrative graphics.

Security-focused guidance for designing RAG applications on AWS, covering data classification, access control on knowledge bases, prompt-injection mitigations, encryption, and auditing of LLM-generated outputs.
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