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Duration 7 hours
Course Outline
Introduction
- Operational use cases for knowledge assistants
- RAG compared with fine-tuning and long context
Knowledge preparation
- Collecting and curating documents
- Chunking strategies and metadata
- Lab: Prepare a document set
Embeddings and vector search
- Embeddings and similarity search
- Vector databases and hybrid search
- Lab: Index and query documents
Answer generation
- Prompting with retrieved context
- Source citations and refusal rules
- Lab: Develop a functional question-and-answer assistant
Integration
- Connecting Microsoft 365, Google Workspace and CRM data
- Chat interfaces and APIs
- Low-code options using n8n
Quality and operations
- Test sets and quality metrics
- Common failure modes: missed passages, outdated content, and incorrect data merging
- Access control, data updates, logging, and cost management
Project planning
- Scoping the initial knowledge assistant implementation
- Roadmap and accountability assignment for agency-specific requirements
Requirements
- Basic programming or scripting experience is recommended; a low-code pathway is available
- Understanding of large language model fundamentals
- Familiarity with document management, databases, and API integration
Target Audience
- Developers, data scientists, and automation engineers developing internal assistants
- IT and innovation teams planning institutional knowledge assistant deployments
- Consultants and product owners responsible for assessing RAG project viability
Testimonials (2)
The extensive selection of tools presented
Miruna Buzduga - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.