Training: Retrieval Augmented Generation (RAG) with LangChain
Level
IntermediateDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedTraining: Retrieval Augmented Generation (RAG) with LangChain
The “Retrieval Augmented Generation (RAG) with LangChain” training is an intensive 2–3-day hands-on workshop that introduces participants to building practical RAG systems — advanced applications that combine information retrieval with generative language models. Participants will learn the complete process of creating such solutions: from data preparation and vector database indexing to integration with LLMs using the LangChain library. With 80% workshop-based learning, attendees will gain real-world skills in constructing RAG applications, significantly reducing hallucinations and improving the quality of AI responses.
What will you learn?
- Prepare and index data for RAG systems in a complete workflow
- Design and implement Retrieval-Augmented Generation pipelines with LangChain
- Master advanced chunking, embedding, and retrieval techniques
- Build efficient Q&A systems and chatbots powered by custom data
- Optimize prompting and manage conversational context
- Create scalable and secure RAG solutions ready for production deployment
Who is this training for?
Programmers and AI engineers who want to build RAG applications-
Data scientists and NLP specialists integrating LLMs with their own data sources
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Analysts and developers interested in practical automation of knowledge access
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System architects exploring modern approaches to combining retrieval and generation
Training Program
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Day 1: Introduction and Data Preparation for RAG
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Module 1: Fundamentals of Retrieval Augmented Generation
- The idea and advantages of RAG compared to standard text generation
- RAG architecture: indexing, retrieval, and generation
- Components and data flow in RAG systems
- Introduction to the LangChain library and its RAG-supporting modules
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Module 2: Data Preparation and Indexing
- Document loading and chunking techniques
- Creating and applying embeddings — representing text in vector space
- Implementing VectorStores for data storage and vector-based search
- Integrating data from various sources (Python code, HTML, PDF, etc.)
- Workshop: indexing your own documents and testing retrieval
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Day 2: Building, Integrating, and Tuning RAG Pipelines
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Module 3: Constructing a Retrieval + Generation Pipeline in LangChain
- Implementing retrievers with different parameters (dense/sparse, BM25)
- Building retriever and LLM components
- Combining retrieval with LLM prompting into a full RAG pipeline
- Implementing a simple Q&A system with RAG
- Using LangGraph for orchestration and application state management
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Module 4: Pipeline Optimization and Personalization
- Prompt tuning, context management, and token limit handling
- Adding conversational history and user context
- Techniques for minimizing hallucinations and ensuring consistency
- Workshop: tuning the pipeline and adding conditional logic
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Day 3: Advanced Applications and Production Deployment
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Module 5: Deploying RAG Applications in Production
- Building APIs and frontends for RAG applications (Flask/FastAPI)
- Working with multimodal data (PDFs, images, etc.) in RAG
- Security, monitoring, and scaling RAG systems
- Safeguards against hallucinations and bias — validation and control
- Practical project: implementing and testing a full RAG application in a chosen scenario
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Module 6: Supporting Tools and the Future of RAG with LangChain
- Integrating LangGraph and LangSmith for debugging and workflow auditing
- Automation and human-in-the-loop approaches for controlled generation
- Trends and emerging opportunities for RAG in the AI ecosystem
- Wrap-up, consultations, and personal development roadmap
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