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Certification: Build and Deploy RAG Chatbots with JavaScript, LangChain.js & AI

Show the world you have AI skills—earn certification in building and deploying RAG chatbots using JavaScript and LangChain.js. Gain practical experience that stands out and demonstrates your expertise in the rapidly evolving field of AI.

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Certification: Build and Deploy RAG Chatbots with JavaScript, LangChain.js & AI

About this certification

The Certification: Build and Deploy RAG Chatbots with JavaScript, LangChain.js & AI provides practical expertise in developing advanced Retrieval-Augmented Generation (RAG) chatbots. Learners will gain skills to build adaptable chatbots that deliver up-to-date, accurate responses, offering a competitive advantage and future-proofing their careers. Enroll today to unlock the potential of AI-powered conversational systems using the latest technologies.

This certification covers the following topics:

  • Understanding RAG Chatbots
  • Technology Stack Overview
  • Building the RAG Chatbot
  • Implementing the User Interface
  • Deploying the Chatbot
  • What is a RAG (Retrieval-Augmented Generation) chatbot, and how does it differ from a standard chatbot like ChatGPT?
  • What are the key technologies and tools used to build the RAG chatbot described in the course?
  • Why is using a RAG approach beneficial for creating a chatbot with current information, like details about Formula 1 racing?
  • What are vector embeddings, and why are they important in a RAG chatbot?
  • How does the chatbot get its knowledge about Formula 1, and how is this knowledge kept up-to-date?
  • How does the chatbot ensure that it's using the retrieved data and not just relying on its pre-existing knowledge to answer questions?
  • What are some potential use cases for a RAG chatbot beyond Formula 1 information?
  • What are the prerequisites for someone who wants to build a similar RAG chatbot following the course?
  • How does a RAG chatbot compare to a standard chatbot in terms of performance and accuracy?
  • What challenges might arise during the data scraping process?
  • Why is text chunking an important step when preparing data for a vector database in a RAG application?
  • What role does the OpenAI API play in the architecture of the RAG chatbot?
  • How does DataStax Astra DB contribute to the functionality of the RAG chatbot?
  • What is the purpose of the useChat hook from Vercel AI in the front-end development of the chatbot?
  • How does RAG compare to traditional keyword-based search methods?
  • How scalable is the RAG chatbot architecture?
  • What are some security concerns when deploying a RAG chatbot, and how can they be addressed?
  • What future developments can we expect in RAG chatbot technology?

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