What you'll learn

Generative AI applications often struggle with scalability, retrieval accuracy, and memory management, making it difficult to build production-ready systems that are performant and cost-efficient. In this course, GenAI Retrieval and Memory Patterns, you’ll gain the ability and the confidence to design, optimize, and scale enterprise-grade GenAI systems using RAG architectures. First, you’ll explore scalable Retrieval-Augmented Generation (RAG) architectures using popular frameworks such as LangChain/LangGraph, Haystack, and AutoGen, learning how to optimize pipelines, manage context windows, and handle thousands of concurrent queries. Next, you’ll discover hybrid search and caching strategies for augmented generation with context, combining dense and sparse retrieval, fusion techniques, and intelligent caching using tools like FAISS, Milvus, and ElasticSearch to maximize performance and relevance. Finally, you’ll learn how to implement memory management and monitoring systems, including short-term, long-term, and working memory patterns, context retrieval policies, and observability using frameworks like LangChain and LlamaIndex for debugging and performance tracking. By the end of this course, you’ll have the skills and knowledge of retrieval, memory, and performance patterns needed to build robust, production-ready and scalable GenAI applications.