LangChain and FAISS for Conversational QA

Building responsive, context-aware AI applications requires a solid understanding of how language models interact with external knowledge bases. This text-based guide teaches you how to design conversational question-answering workflows step by step using LangChain and FAISS vector search. You will move from fundamental retrieval concepts to structuring complete conversational pipelines that remember dialogue history and retrieve accurate context from stored documents. What you'll learn: - Understand core concepts of large language model orchestration, embeddings, and vector similarity search - Configure FAISS vector stores to index, chunk, and retrieve custom document collections efficiently - Implement retrieval-augmented generation (RAG) architectures to deliver grounded, relevant answers - Manage conversational memory and dialogue history within LangChain pipelines - Apply prompt engineering best practices to guide response quality and formatting - Test and refine conversational flows using structured text-based code exercises The course begins with foundational terminology and architecture overviews before advancing into hands-on code walkthroughs and retrieval workflows. It is designed for developers and AI enthusiasts of all experience levels with basic Python familiarity. Start reading today to build reliable conversational tools with modern retrieval techniques.