Retrieval-Augmented Generation (RAG) for AI Agents

Unlock the next generation of AI applications by mastering Retrieval-Augmented Generation (RAG) for AI Agents. This comprehensive video series transforms you from an LLM novice into a skilled practitioner capable of building intelligent agents that access real-time knowledge, maintain conversational context, and eliminate hallucinations through semantic information retrieval. Begin with foundational concepts: understand why traditional LLMs fall short without external tools, explore context window limitations, and discover how semantic search fundamentally outperforms keyword matching. Lessons 1-3 provide hands-on implementation using Weaviate vector database and DeBERTa embeddings. You'll learn Docker deployment on Windows, CUDA GPU acceleration, and construct a functional patent-search agent using LangGraph that dynamically retrieves relevant abstracts and descriptions from USPTO data sources. Progress to advanced techniques in Lessons 4-6, where you'll implement token-level RAG using Milvus and ColBERT. This revolutionary approach stores individual token embeddings with contextual nuance, enabling granular control over search relevance. Learn to emphasize critical terms while de-emphasizing generic words, dramatically improving precision through amplitude weighting. Through step-by-step coding demonstrations, you'll master end-to-end vectorization pipelines, multi-stage agent orchestration, and seamless LLM integration with DeepSeek Chat. By course completion, you'll have built both simple chunk-based and sophisticated token-level RAG systems, ready to deploy production-grade customer support bots, research assistants, and domain-specific agents that ground their decisions in your proprietary data with remarkable accuracy.