Retrieval-Augmented Generation (RAG)

Techpratham's Retrieval-Augmented Generation (RAG) course is meant to teach students how to use advanced AI methods to create text that is based on knowledge. Retrieval pipelines, vector databases, embeddings, and LLM integration are all covered in the training. You'll learn how to use tools like Pinecone, FAISS, and LangChain to build RAG-based solutions that can grow. By the end, students will know how to make RAG systems for chatbots, search engines, and decision-support tools that work for big businesses.

This Retrieval-Augmented Generation (RAG) course provides practical training in building LLM-powered AI applications that retrieve relevant information from external knowledge sources and generate accurate, context-aware responses. Learners explore document ingestion, chunking, semantic search, vector retrieval, and end-to-end RAG workflows.

The course also covers advanced RAG techniques such as hybrid search, metadata filtering, reranking, query processing, and context optimization to improve retrieval quality and response relevance across real-world RAG systems.

Learners also explore RAG evaluation, production deployment, monitoring, security, privacy, and governance practices for building reliable AI applications that work with enterprise and domain-specific knowledge.