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

About this course

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 LearnReal-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.<

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