RAG Course Online

Enroll now in our RAG course to gain hands-on experience through live interactive sessions, practical labs, real-world RAG use cases, and expert-led sessions. This course is fully aligned with the latest advancements in generative AI and retrieval-based architectures preparing you to build scalable intelligent systems that combine the power of search with LLM reasoning.

Prerequisites

• Python Programming Knowledge
• Machine Learning Concepts
• Basic NLP knowledge
• Knowledge of Large Language Models (LLMs)

What You Will Learn

• What is Retrieval-Augmented Generation?
• Use cases: Chatbots, document Q&A, customer support, etc.
• Vector vs. sparse retrieval
• Similarity measures (Cosine, Dot product, etc.)
• Overview of sentence transformers, OpenAI embeddings, etc.
• Introduction to vector databases
• Choosing a vector DB: FAISS, Chroma, Pinecone, Qdrant, etc.
• Prompt engineering basics
• Understanding hallucinations
• Architecture of a RAG system
• Retrieval pipeline setup
• Open-source frameworks: LangChain, LlamaIndex, Haystack
• Hybrid retrieval (dense + sparse)
• Context filtering and prioritization
• Feedback loops (RLHF or human-in-the-loop)
• Hallucination detection and mitigation
• Integrating with front-end apps (Streamlit, React, etc.)
• Monitoring and observability