Mar 13
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RAG, Retrieval Augmented Generation, AI Agents, Vector Database, Embeddings, Semantic Search, ReAct, Function Calling, LangChain, LlamaIndex, BM25, AI Engineering, Computer Science Course. Video Description Discover the two most powerful design patterns in modern AI development: Retrieval-Augmented Generation (RAG) and Autonomous Agents. In this lecture, based on Chapter 6 of "AI Engineering," we explore how to give models access to external knowledge and the ability to act on the world. Perfect for CS students and developers, this video covers the architecture, algorithms, and planning strategies needed to build advanced AI systems. Key Topics Covered: • RAG Architecture: Learn how RAG enhances a model's generation by retrieving relevant information from external sources like documents or databases, overcoming context window limitations. • Retrieval Algorithms: We break down the difference between Term-based retrieval (like BM25/TF-IDF) and Embedding-based retrieval (Semantic Search). Understand how Vector Databases use algorithms like HNSW and IVF to perform fast nearest-neighbor searches. • Retrieval Optimization: Master techniques to improve RAG performance, including advanced Chunking Strategies, Query Rewriting, Reranking, and Contextual Retrieval. • AI Agents & Tools: Explore how Agents differ from RAG by having the ability to use tools (read and write actions) and plan complex tasks. We discuss Function Calling and how agents perceive and act upon their environments. • Planning & Reflection: Understand how agents solve problems using reasoning patterns like ReAct (Reason + Act) and Reflexion, and how to decouple planning from execution to reduce errors. • Memory Systems: Learn how to implement Short-term and Long-term memory mechanisms to manage information overflow and persist user preferences across sessions. Resources: • This video covers Chapter 6: "RAG and Agents". • Learn how to build systems that go beyond static prompts to interact with data and tools dynamically.