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: Fundamentals of ML Model Security
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Module 1: Introduction to ML ecosystem threats
- Characteristics of modern AI model attacks
- Consequences of successful breaches
- Case studies of intrusions and manipulations in real-world projects
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Module 2: Types of attacks on ML models
- Adversarial attacks: methods of generating adversarial samples
- Attacks on training data privacy
- Information leakage from trained models
- Vulnerability analysis of different ML architectures
- Attacks targeting ML infrastructure
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Module 3: Workshop – Threat identification
- Simulating attacks on sample classification and regression models
- Analyzing traces and penetration mechanisms of ML models
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Day 2: Advanced Protection Techniques
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Module 4: Methods for securing ML models
- Adversarial training techniques
- Federated learning for enhanced privacy
- Implementing obfuscation and data privacy mechanisms
- Strategies for risk reduction in ML workflows
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Module 5: Workshop – Practical model protection
- Designing resilient ML architectures
- Implementing advanced defense techniques
- Security testing of ML models
- Developing security policies for ML teams
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Module 6: Security tools and frameworks
- Overview of open-source tools for model protection
- Analysis of specialized ML cybersecurity libraries
- Automating security verification processes
- Integrating security tools with ML pipelines
