In today’s AI-driven world, traditional language models often struggle with outdated or incomplete knowledge. The “Retrieval Augmented Generation (RAG) with LangChain” course equips learners with the skills to enhance AI models by integrating external knowledge sources for more accurate and context-aware responses. This course delves into LangChain, a powerful framework that enables seamless retrieval of relevant data from vector databases, improving the performance and reliability of AI applications. Whether you’re looking to refine chatbot interactions, optimize content generation, or build knowledge-intensive applications, this course provides the essential tools and expertise to get started!
Before diving into Retrieval Augmented Generation (RAG) with LangChain, it’s recommended to complete Developing LLM Applications with LangChain. This prerequisite course provides essential knowledge of LangChain’s core components, including prompts, chains, and agents, which will help you better understand and implement RAG workflows effectively.
Course Overview
This intermediate-level course is for developers and AI enthusiasts seeking to implement Retrieval Augmented Generation (RAG) in their applications. With 3 hours of content, 10 videos, and 33 hands-on exercises, participants will gain practical experience in embedding storage, retrieval workflows, vector databases, and RAG optimization techniques.
What You’ll Learn
1. Introduction to Retrieval Augmented Generation (RAG)
- Understand the limitations of standalone Large Language Models (LLMs).
- Learn how RAG enhances AI applications by integrating real-time, external knowledge retrieval.
- Explore the LangChain ecosystem and its role in implementing RAG.
2. Working with Vector Databases & Embeddings
- Learn how to store and retrieve knowledge efficiently using vector embeddings.
- Explore popular vector databases like FAISS, Pinecone, and ChromaDB.
- Implement text embedding models for effective information retrieval.
3. Implementing the RAG Workflow with LangChain
- Discover the step-by-step RAG pipeline from data preprocessing to retrieval.
- Learn how to tokenize and embed data for efficient storage and querying.
- Use LangChain to integrate retrieval mechanisms with LLMs.
4. Optimizing RAG for Performance & Accuracy
- Fine-tune retrieval methods for relevant, high-quality responses.
- Implement chunking strategies and metadata filtering for improved search accuracy.
- Optimize chatbot interactions using memory functions and efficient query handling.
Why Take This Course?
Mastering Retrieval Augmented Generation (RAG) enables you to overcome LLM limitations, ensuring AI applications deliver accurate, real-time, and contextually aware responses. Whether you’re a developer, data scientist, or AI enthusiast, this course provides hands-on experience in building scalable, knowledge-rich AI solutions. You’ll gain expertise in embedding models, vector databases, retrieval strategies, and LangChain-powered AI workflows, giving you a competitive edge in the AI landscape.
Get Started Today!
With expert-led instruction, real-world projects, and interactive exercises, this course is your gateway to building next-level AI applications with RAG and LangChain. Enroll now and start transforming the way AI interacts with information!
