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AI Agent Memory & RAG is a practical course designed to help learners build intelligent, context-aware AI agents using memory systems, Retrieval-Augmented Generation, Agentic RAG, vector databases, semantic search, and knowledge retrieval.
The course covers how AI agents store, retrieve, manage, and update information across interactions using short-term, long-term, persistent, episodic, and semantic memory approaches. Learners also explore how Retrieval-Augmented Generation connects AI agents with external knowledge sources to produce more relevant and grounded responses.
Learners progress from RAG fundamentals and retrieval pipelines to embeddings, vector databases, semantic search, hybrid retrieval, reranking, advanced retrieval strategies, Agentic RAG, memory-enhanced RAG, and multi-agent knowledge workflows.
The program also covers RAG evaluation, observability, reliability, security, enterprise knowledge systems, production optimization, and practical implementation patterns. Through hands-on projects, learners build memory-aware and retrieval-enabled AI agents capable of using contextual information, retrieving relevant knowledge, reasoning over information, and supporting complex multi-step workflows.
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