RAG-Based Chatbots with LLMs- Paid

Categories: Data Science & AI
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About Course

This hands-on course teaches you how to build powerful RAG (Retrieval-Augmented Generation) systems that combine the strengths of LLMs with dynamic external knowledge. You’ll learn to extract information from custom documents, generate embeddings, integrate vector databases like FAISS and Pinecone, and deploy responsive chatbots that answer contextually. Perfect for developers aiming to build smart, memory-aware AI assistants for customer support, HR, finance, legal, and more.

📌 Prerequisites:

  • Basic understanding of Python

  • Familiarity with REST APIs

  • Basics of machine learning and NLP concepts

  • Optional: Knowledge of LangChain or vector DBs is a plus

📅 Duration: 8 Weeks | 💼 Internship: Optional
🧩 Add-ons: 📜 Certificate of Completion | 💼 Internship Certificate
💰 Price: ₹40,000

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What Will You Learn?

  • What RAG is and why it’s essential for knowledge-specific LLMs
  • How to convert documents into searchable vectors
  • Build a full RAG pipeline using LangChain or Haystack
  • Use FAISS, Pinecone, or ChromaDB to enable smart retrieval
  • Deploy and test a RAG-based chatbot in production

Course Content

Introduction to RAG

  • What is RAG and why it’s important
  • Limitations of base LLMs
  • Use cases (Document QA, Legal AI, HR bots)

Document Processing & Embeddings

Vector Databases Deep Dive

Retriever + Generator Architecture

LangChain/Haystack Implementation

Chat UI and Frontend Integration

Deployment

Capstone projects

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