Enroll Course: https://www.coursera.org/learn/project-generative-ai-applications-with-rag-and-langchain
Are you ready to move beyond theory and dive into building your own real-world Generative AI applications? Coursera’s ‘Project: Generative AI Applications with RAG and LangChain’ is the perfect guided project to solidify your Gen AI engineering skills. This hands-on course is designed to test and apply everything you’ve learned, culminating in the creation of your very own AI application.
The project starts by filling in any knowledge gaps regarding document loaders within LangChain. You’ll learn how to efficiently load documents from various sources, a crucial first step in preparing data for your AI model. The syllabus then delves into text splitting strategies, a vital technique for optimizing Retrieval Augmented Generation (RAG) and enhancing model responsiveness. Through practical labs, you’ll get to practice these document loading and text-splitting techniques firsthand.
The core of the project revolves around RAG using LangChain. You’ll master the art of storing embeddings using vector stores, with a specific focus on Chroma DB. The course explores various LangChain retrievers, including Vector Store-Based, Multi-Query, Self-Query, and Parent Document Retriever, providing a comprehensive understanding of how to fetch the most relevant information. Hands-on labs will guide you through preparing documents for embedding, utilizing watsonx.ai for embedding generation, and storing these embeddings in vector databases like Chroma DB and FAISS. Ultimately, you’ll learn to use these retrievers to efficiently extract pertinent document segments.
The final module focuses on creating a QA Bot to read your documents. Here, you’ll implement RAG to significantly improve retrieval accuracy. You’ll become acquainted with Gradio, a user-friendly tool for setting up simple interfaces to interact with your models. The ultimate goal is to construct a robust QA bot capable of answering questions directly from your loaded documents using LangChain and Large Language Models (LLMs). The hands-on labs in this section allow you to practice setting up the Gradio interface and building your QA bot. The culmination of this project is the construction of a complete AI application, demonstrating your newfound expertise.
This project is highly recommended for anyone looking to gain practical, real-world experience in building generative AI applications. The structured approach, combined with practical labs and a tangible final project, makes it an invaluable learning experience.
Enroll Course: https://www.coursera.org/learn/project-generative-ai-applications-with-rag-and-langchain