• Duration

    30 Minutes

  • Level

    Intermediate

  • Course Type

    Short Course

What you'll Learn

  • Understand the fundamentals of RAG.

  • Gain in-depth knowledge of LangChain.

  • Hands-on experience in building QA RAG systems.

About the Instructor

Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya

Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence.
About the Instructor

Who Should Enroll?

  • Individuals looking to enhance their expertise in AI-driven QA systems and explore the capabilities of LangChain.

  • For those on their journey to mastering AI and NLP, ready to explore advanced frameworks and make a mark in the tech world.

FAQ

  • What is LangChain?

    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.

  • What is a QA RAG system?

    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.

  • What is the difference between LangChain and other NLP frameworks?

    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.

  • Does this course include a certificate of completion?

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

  • Will I build a project during this course?

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