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?