Model Live Course
Duration
Modules 6 modules

Course Description

Instructor

In today's era of applied AI, expectations for the speed, scale, and accuracy of information access have never been higher. Organizations want to enrich large language models (LLMs) with their own enterprise data to both improve response quality and produce reliable, citable outputs. This fast-paced 3-hour "Retrieval-Augmented Generation (RAG) with LangChain Workshop" offers participants a blend of theory and practice for standing up a RAG architecture from scratch.

The workshop covers the core concepts of RAG, how to orchestrate components with LangChain, and aims to build a live, working document-based chatbot by the end of the session. Participants will experience the end-to-end document processing, embedding, and vector search chain, directly observing the speed, cost, and traceability advantages that RAG brings compared to long-context approaches.

Who Should Attend

  • Data scientists, MLOps engineers, software developers – those looking to build LLM-based applications

  • Technical leaders aiming to bring the practical benefits of RAG architecture to their organization

  • Professionals seeking tangible outputs in a short time through a learn-build-demo cycle

Delivering intensive learning within a focused timeframe, this workshop will rapidly advance participants on the RAG journey from "text to knowledge" and ensure you leave with a live-demoed project built on real data.

Curriculum

What Will We Cover?

  • What will be built and what is out of scope

  • Live demo: What kind of application we will have developed by the end of the day

  • Environment setup
Retrieval-Augmented Generation Fundamentals

  • Understanding RAG architecture and its advantages

  • RAG vs. Long Context Models

  • Core components: Document processing, embeddings, vector storage, and retrieval

  • Real-world RAG application examples and use cases
Introduction to LangChain

  • LangChain fundamentals

  • Document loaders and text chunking strategies

  • Embedding models and vector databases

  • Retrievers and query transformation methods

  • Integration with LLMs
Building the Chatbot

  • Setting up the project structure

  • Building the document ingestion pipeline

  • Creating and configuring the vector store

  • Vector similarity metrics

  • Designing effective prompts for RAG

  • Building the retrieval chain

  • Implementing the application UI with Gradio
Deployment

  • Making your application production-ready

  • Environment variables and security best practices

  • Deploying the model

  • Secure management of API keys

  • Testing your live environment
Wrap-Up and Next Steps

  • Handling large document collections

  • Latency reduction strategies

  • Cost optimization approaches

  • Enterprise-grade considerations

  • Multimodal RAG

  • Fine-Tuning

  • Agentic RAG

  • Summary of what was accomplished

  • Additional resources and documentation

  • Q&A session

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