What will you learn in Fundamentals of Retrieval-Augmented Generation with LangChain Course
- Core RAG principles & architecture: Understand how retrieval augments LLMs to ground responses in real-time external data.
- LangChain pipeline implementation: Indexing, retrieval, and generation techniques using LangChain with hands-on teaching.
- Frontend integration using Streamlit: Learn to build a user-facing RAG chatbot interface that connects LangChain with Streamlit.
- Advanced RAG challenges: Tackle extended use cases like multiple file formats and switching vector stores in real-world contexts.
Program Overview
Module 1: Getting Started with RAG
⏳ ~30 minutes
Topics: Introduction to RAG architecture and benefits over pure LLM approaches.
Hands-on: Explore quiz-based theory to understand RAG principles.
Module 2: RAG Basics
⏳ ~60 minutes
Topics: Components: retriever, index creation, document querying.
Hands-on: Build a basic indexing and retrieval pipeline; quiz to reinforce learning.
Module 3: RAG with LangChain
⏳ ~60 minutes
Topics: Use LangChain for document indexing, augmented query construction, and response generation.
Hands-on: Code pipeline using LangChain and validate via an interactive quiz.
Module 4: Frontend with Streamlit
⏳ ~45 minutes
Topics: Streamlit app structure, UI elements for chat interaction with RAG system.
Hands-on: Build a basic RAG-powered chatbot UI and test retrieval responses.
Module 5: Advanced RAG Challenges
⏳ ~60 minutes
Topics: Handle challenges like switching between vector stores and supporting PDFs.
Hands-on: Implement solutions for multi-file and format ingestion using LangChain and vector store APIs.
Module 6: Conclusion
⏳ ~15 minutes
Topics: Course wrap-up, best practices, and applying RAG in production.
Hands-on: Final overview quiz and application walk-through.
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Job Outlook
- Highly relevant skill: RAG is crucial for LLM app development in search, chatbots, and knowledge systems.
- Engineering leverage: Enables developers to design grounded, accurate LLM-powered solutions with retrieval pipelines.
- Career applicability: Ideal for roles such as LLM Engineer, AI Developer, and RAG-focused software engineer roles.
- Portfolio-ready: Projects using LangChain + Streamlit demonstrate practical expertise in generative AI app development.
Specification: Fundamentals of Retrieval-Augmented Generation with LangChain
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