Build a QA RAG system with Langchain

  • IntermediateLevel

  • 1799+Students Enrolled

  • 30 MinsDuration

  • 5Average Rating

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About this Course

  • Master building a powerful QA Retrieval-Augmented Generation (RAG) system using LangChain, focusing on data retrieval and AI-driven solutions.
  • Gain a deep understanding of LangChain, learning how to integrate it for advanced data retrieval and natural language processing tasks.
  • Acquire hands-on experience in building and deploying QA RAG systems, enhancing your practical skills for real-world AI applications.

Learning Outcomes

Master RAG System Design

Learn to build a powerful QA RAG system using LangChain.

Deep Dive into LangChain

Gain in-depth knowledge of LangChain for data retrieval and NLP in AI.

Hands-On RAG Experience

Build and deploy QA RAG systems, enhancing real-world AI skills.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. Hands On: Build a QA RAG System with Langchain

  2. 2. Course Handouts

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Dipanjan Sarkar

Principal AI Scientist

Dipanjan Sarkar, Lead Data Scientist, Author & Consultant, has 10+ years of expertise in ML, DL, GenAI, CV & NLP. He has led AI initiatives across Fortune 100 firms & startups, building data products & upskilling professionals at all levels

Get this Course Now

With this course you’ll get

  • 30 Mins

    Duration

  • Dipanjan Sarkar

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility
certificate

Frequently Asked Questions

Looking for answers to other questions?

LangChain is a framework for building applications with large language models (LLMs), offering tools for prompt chaining, memory management, and integration with APIs and databases, ideal for generative AI tasks like RAG and chatbots

A QA RAG system is a Question-Answering system that combines retrieval mechanisms with generative models to provide accurate and contextually relevant answers. It retrieves relevant information from a large dataset and generates answers using a language model.

LangChain focuses on building workflows with LLMs, offering prompt engineering, chaining, and memory management for generative AI tasks. Unlike general NLP frameworks like NLTK or Hugging Face, it excels in integrating tools like vector databases and APIs, making it ideal for retrieval-augmented generation and dynamic task execution.

Yes, you will receive a certificate of completion upon successfully finishing the course and all associated assessments

Yes, you’ll build a fully functional QA RAG system that integrates an LLM with a vector database for document retrieval.

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