About this Training

This advanced hands-on training covers Retrieval Augmented Generation (RAG), fine-tuning strategies, and Model Context Protocols to help data practitioners design reliable AI solutions. Participants will learn how to build retrieval pipelines, integrate knowledge sources with large language models, fine-tune models for domain tasks, and structure model context for safer and more accurate outputs. Through practical labs and real world projects, attendees will be able to deploy RAG systems, evaluate model performance, and produce repeatable workflows for AI driven data products.

What You'll Learn

• Module 1: Introduction to RAG and Retrieval – Fundamentals of retrieval based generation and knowledge augmentation
• Module 2: Vector Databases and Embeddings – Creating embeddings and using vector stores for search
• Module 3: Retrieval Pipelines and Indexing – Building retrieval pipelines and indexing strategies
• Module 4: Prompting and Context Design – Designing prompts and structuring model context for reliable outputs
• Module 5: Fine Tuning and Adaptation – Approaches to fine tuning models and task adaptation
• Module 6: Model Context Protocols (MCP) – Protocol patterns for managing context and safety constraints
• Module 7: Evaluation and Monitoring – Metrics for RAG systems and monitoring model behavior in production
• Module 8: Real-World Project – Implementing a RAG system end to end for a practical dataset