Building RAG Pipelines with LLMs

Aim

Program Objectives

Program Structure

Week 1: Foundations of Retrieval-Augmented Generation
Module 1: Introduction to RAG Systems

• Chapter 1.1: What is Retrieval-Augmented Generation?

• Chapter 1.2: Components of a RAG Pipeline

• Chapter 1.3: Benefits and Limitations of RAG

Module 2: Understanding Retrieval and Vector Databases

• Chapter 2.1: Dense vs. Sparse Retrieval

• Chapter 2.2: Vector Embeddings and Semantic Search

• Chapter 2.3: Overview of Tools (FAISS, Weaviate, Pinecone, Qdrant)

Week 2: Building the Core RAG Stack
Module 3: Integrating LLMs with Search

• Chapter 3.1: Embedding Generation (OpenAI, Hugging Face)

• Chapter 3.2: Chunking and Preprocessing Strategies

• Chapter 3.3: Prompt Templates for RAG

• Chapter 3.4: Connecting LLMs to Vector DBs

Module 4: RAG System Implementation

• Chapter 4.1: Document Ingestion and Indexing

• Chapter 4.2: Query Handling and Retrieval Flow

• Chapter 4.3: Response Synthesis using LLMs

• Chapter 4.4: Evaluation Metrics for RAG Responses

Week 3: Optimization, Deployment, and Projects
Module 5: Advanced RAG Techniques

• Chapter 5.1: Hybrid Search (BM25 + Embeddings)

• Chapter 5.2: RAG with Structured and Unstructured Data

• Chapter 5.3: Multi-turn and Conversational RAG

Module 6: Deployment and Capstone

• Chapter 6.1: Deploying RAG Systems with LangChain or LlamaIndex

• Chapter 6.2: Monitoring, Caching, and API Design

• Chapter 6.3: Capstone Project – Build Your Own RAG Pipeline

• Chapter 6.4: Industry Use Cases and Future Trends

Who Should Enrol?

Program Outcomes

Fee Structure

We accept 20+ global currencies. View list →

What You’ll Gain