Overview
The Retrieval-Augmented Generation (RAG) course provides a practical introduction to building AI applications that combine Large Language Models (LLMs) with external knowledge sources to generate more accurate, contextual, and reliable responses. Learners will understand how retrieval mechanisms enhance AI outputs by connecting models with documents, databases, and enterprise knowledge repositories.
This hands-on course explores RAG architecture, document processing, embeddings, vector databases, retrieval strategies, and AI application workflows. Participants will gain practical knowledge to design intelligent AI systems for search, knowledge management, question answering, and enterprise automation.
By the end of the course, learners will be able to design and implement Retrieval-Augmented Generation pipelines that improve AI performance and deliver context-aware results.
- Software Developers building AI-powered applications and knowledge systems
- AI Engineers and Machine Learning Professionals implementing retrieval-based AI solutions
- Python Developers expanding into Generative AI application development
- Data Engineers and Analytics Professionals managing AI-ready data pipelines
- IT Professionals and Solution Architects integrating AI with enterprise knowledge systems
- Product Managers exploring AI-driven product capabilities
- Automation Specialists developing intelligent information retrieval workflows
- Entrepreneurs and Consultants creating AI-enabled business solutions
- Students and Emerging Technology Learners developing practical AI skills
- Technical Teams adopting enterprise AI and knowledge retrieval architectures
- Introduction to Retrieval-Augmented Generation (RAG)
- Understanding how RAG enhances Large Language Models (LLMs)
- RAG architecture and end-to-end workflow concepts
- Data ingestion and document processing fundamentals
- Text chunking and document segmentation strategies
- Embeddings and semantic search concepts
- Vector databases and indexing fundamentals
- Similarity search and retrieval mechanisms
- Query transformation and retrieval optimization techniques
- Prompt engineering for retrieval-based systems
- Context injection and response generation workflows
- Knowledge base design and management
- Building question-answering and search applications
- Hybrid retrieval approaches and advanced retrieval techniques
- Memory and context management in RAG systems
- Evaluating retrieval quality and output accuracy
- Monitoring, debugging, and optimizing RAG pipelines
- Security, governance, and responsible AI practices
- Real-world business use cases and enterprise implementations
- Multiple-choice questions
- Scenario-based prompt design questions
- Practical prompt-writing exercises
- Output evaluation and improvement tasks
- Recommended passing score as defined by training provider
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Training Options
Corporate Training
We work with customers to provide tailor made training solutions, onsite and off site delivery with customized content to cover areas of key importance. Please contact for private batches or any other requirements.