This Retrieval Systems, RAG, and Agentic Workflows course equips you with the skills to build intelligent AI systems that retrieve, reason, and respond using real-world data. You'll work with tools like ChromaDB, Pinecone, LangChain, LangFuse, and Python to design production-ready retrieval and agent pipelines.

In Module 1, you'll explore the fundamentals of Retrieval-Augmented Generation (RAG), learning how to connect language models with external knowledge using embeddings and similarity search. Module 2 dives into vector databases and semantic search, where you'll build vector indexes, query semantic data, and evaluate performance tradeoffs across platforms like Chroma, Pinecone, and FAISS. Module 3 focuses on conversational agents and workflows — you'll design state-based dialogue systems, manage session memory, and build multi-step Q&A chatbots powered by RAG. In Module 4, you'll master optimization, debugging, and observability, using tools like LangFuse and OpenTelemetry to diagnose issues, visualize latency, and improve agent outputs through reranking and query routing. By the end of this course, you will: - Build end-to-end RAG pipelines that connect LLMs with external knowledge sources - Design and query vector databases for context-aware semantic search - Create conversational agents with memory, state management, and retrieval-driven Q&A - Debug, optimize, and monitor AI agent workflows using observability tools Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.