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Gen AI – RAG Pipeline

Master Retrieval-Augmented Generation from the ground up. Learn how to build, evaluate and deploy production-grade RAG pipelines that connect LLMs with your own data — using LangChain, vector databases and advanced retrieval strategies.

⏱ 8 Weeks 📶 Intermediate 🌐 Online & Offline 🛠 Project-Based 📜 Certificate
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Course Fee
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✔ 8 Weeks of Live Instruction
✔ Build Production RAG Systems
✔ Recorded Session Access
✔ Doubt Clearing Sessions
✔ Completion Certificate
✔ Online & Offline Batch Options
Curriculum Overview

What You'll Learn

End-to-end RAG pipeline construction — from embeddings to production deployment.

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Introduction to Generative AI & LLM landscape
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Transformer Architecture — Attention, Tokens & Embeddings
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Text Embeddings — OpenAI, HuggingFace Sentence Transformers
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Vector Databases — ChromaDB, Pinecone, FAISS & Weaviate
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RAG Architecture — Naive, Advanced & Modular RAG
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Document Loading & Chunking Strategies
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Retrieval Techniques — Semantic, BM25 & Hybrid Search
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LangChain RAG Chains & RetrievalQA
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Prompt Engineering for RAG — Context Injection & Grounding
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RAG Evaluation — Ragas, Faithfulness & Answer Relevancy
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Multi-Document RAG & Conversational RAG
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Capstone: Deploy a Production Q&A RAG App
Week by Week

Course Curriculum

MODULE 01 Gen AI & LLM Foundations
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Gen AI landscape — GPT-4, Claude, Gemini, Llama
Transformer architecture overview — Self-attention & Feed-forward
Tokenisation, Context Windows & Temperature
LLM API Integration — OpenAI Python SDK
Prompt Engineering — Zero-shot, Few-shot, Chain-of-Thought
MODULE 02 Embeddings & Vector Databases
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What are Embeddings? Cosine Similarity & Dot Product
OpenAI text-embedding-3-small / Sentence Transformers
ChromaDB — Setup, Ingest & Query
Pinecone — Cloud Vector DB with Metadata Filtering
FAISS for local similarity search
MODULE 03 RAG Architecture & Chunking
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Naive RAG vs Advanced RAG vs Modular RAG
Document Loaders — PDF, DOCX, HTML, Web Pages
Chunking Strategies — Fixed, Recursive, Semantic
Metadata Tagging & Filtering for Better Retrieval
LangChain RetrievalQA & Conversational Retrieval Chain
MODULE 04 Advanced Retrieval & Evaluation
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Hybrid Search — BM25 + Semantic with Reciprocal Rank Fusion
Re-ranking with Cross-Encoders
HyDE — Hypothetical Document Embeddings
Ragas Evaluation — Faithfulness, Context Precision, Answer Relevancy
Hallucination Detection & Guardrails
MODULE 05 Production Deployment & Capstone
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Multi-Document RAG & Conversational Memory
Building a Streamlit / Gradio RAG UI
FastAPI Backend for RAG API
Docker + Cloud Deployment
Capstone: Production Q&A App on your own documents

Build the Future of AI Apps

RAG is the backbone of enterprise GenAI. Master it with R2 AI TECH.

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