Project: Generative AI Applications with RAG and LangChain

This course is part of multiple programs. Learn more

Instructors: Kang Wang +1 more

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What you'll learn

  •    Gain practical experience building your own real-world generative AI application to showcase in interviews
  •   Create and configure a vector database to store document embeddings and develop a retriever to fetch relevant segments based on user queries
  •   Set up a simple Gradio interface for user interaction and build a question-answering bot using LangChain and a large language model (LLM)
  • Skills you'll gain

  •   Databases
  •   Natural Language Processing
  •   Large Language Modeling
  •   User Interface (UI)
  •   Generative AI
  • There are 3 modules in this course

    You’ll begin by filling in key knowledge gaps, such as using LangChain’s document loaders to ingest documents from various sources. You’ll then explore and apply text-splitting strategies to improve model responsiveness and use IBM watsonx to embed documents. These embeddings will be stored in a vector database, which you’ll connect to LangChain to develop an effective document retriever. As your project progresses, you’ll implement retrieval-augmented generation (RAG) to enhance retrieval accuracy, construct a question-answering bot, and build a simple Gradio interface for interactive model responses. By the end of the course, you’ll have a complete, portfolio-ready AI application that showcases your skills and serves as compelling evidence of your ability to engineer real-world generative AI solutions. If you're ready to elevate your career with hands-on experience, enroll today and take the next step toward becoming a confident AI engineer.

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