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

<div>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.</div><div><br></div><div>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.</div><div><br></div><div>What You’ll Learn</div><div><ul><li><span style="font-size: 1rem;">Real-World Project: Build two end-to-end RAG Projects on Company Data and E-Commerce Semantic Search.</span></li><li><span style="font-size: 1rem;">Caching Strategies: Use embedding and response caching to reduce cost, latency, and improve efficiency.</span></li><li><span style="font-size: 1rem;">Indexing: Explore Flat, IVF Flat, HNSW, and disk-based indexes; learn which one to use for your dataset.</span></li><li><span style="font-size: 1rem;">Reranking: Improve answer precision using similarity scores, cross-encoders, and LLM-based reranking.</span></li><li><span style="font-size: 1rem;">Evaluations (Evals &amp; Ragas): Measure faithfulness, relevance, and retrieval quality with Ragas metrics.</span></li><li><span style="font-size: 1rem;">Metadata: Use metadata filters to make retrieval precise, context-aware, and production-ready.</span></li></ul></div><div><br></div><div>Why Take This Course?</div><div><ul><li><span style="font-size: 1rem;">It’s hands-on — you won’t just learn theory; you’ll build working RAG pipelines.</span></li><li><span style="font-size: 1rem;">You’ll learn best practices for scaling from demo to production.</span></li><li><span style="font-size: 1rem;">Content is designed for real-world applications in enterprise, startups, and research.</span></li><li><span style="font-size: 1rem;">You’ll walk away with code, skills, and confidence to build your own RAG-powered apps.</span></li></ul></div><div><br></div><div>Who This Course Is For</div><div><ul><li><span style="font-size: 1rem;">Developers and data scientists interested in LangChain and LLM applications.</span></li><li><span style="font-size: 1rem;">AI/ML engineers who want to deploy production-ready RAG systems.</span></li><li><span style="font-size: 1rem;">Professionals curious about vector databases, embeddings, and retrieval systems.</span></li><li><span style="font-size: 1rem;">Anyone who wants to go beyond ChatGPT and build AI that leverages their own data.</span></li></ul></div><div><br></div><div>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.</div>

What you'll learn:

• Set up PostgreSQL with the pgvector extension to enable efficient vector search
• Build an end to end RAG pipeline connecting Large Language Models LLMs with PostgreSQL
• Implement a RAG pipeline step-by-step: retrieval, context injection, and grounded LLM responses.
• Explore vector indexes (HNSW, IVFFlat) and learn how they improve retrieval speed and accuracy.
• Apply semantic caching to reduce cost and latency while improving response times.
• Evaluate RAG systems using RAGAS metrics like faithfulness, context recall, and precision.
• Enhance retrieval quality with re-ranking and metadata filtering.
• Deploy APIs for ingestion and RAG Q&A to make your projects production-ready and testable.