Hands-On RAG with LangChain: Build Real-World Projects

Retrieval-Augmented Generation (RAG) is one of the most powerful ways to make Large Language Models (LLMs) smarter, more reliable, and production-ready. Instead of depending only on what the model “knows,” RAG allows us to fetch relevant knowledge from external sources and provide precise, up-to-date answers. In this hands-on course, you’ll go beyond the basics and actually build RAG pipelines step by step using LangChain, the leading framework for LLM applications. Whether you are a developer, data scientist, or AI enthusiast, this course will give you the practical skills to design, implement, and optimize real-world RAG projects. What You’ll Learn Real-World Project: Build two end-to-end RAG Projects on Company Data and E-Commerce Semantic Search. Caching Strategies: Use embedding and response caching to reduce cost, latency, and improve efficiency. Indexing: Explore Flat, IVF Flat, HNSW, and disk-based indexes; learn which one to use for your dataset. Reranking: Improve answer precision using similarity scores, cross-encoders, and LLM-based reranking. Evaluations (Evals & Ragas): Measure faithfulness, relevance, and retrieval quality with Ragas metrics. Metadata: Use metadata filters to make retrieval precise, context-aware, and production-ready. Why Take This Course? It’s hands-on — you won’t just learn theory; you’ll build working RAG pipelines. You’ll learn best practices for scaling from demo to production. Content is designed for real-world applications in enterprise, startups, and research. You’ll walk away with code, skills, and confidence to build your own RAG-powered apps. Who This Course Is For Developers and data scientists interested in LangChain and LLM applications. AI/ML engineers who want to deploy production-ready RAG systems. Professionals curious about vector databases, embeddings, and retrieval systems. Anyone who wants to go beyond ChatGPT and build AI that leverages their own data. By the end of this course, you’ll have the knowledge and hands-on experience to design and implement efficient RAG pipelines with LangChain — and the skills to apply them to your own projects or business use cases.