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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  • Common failure modes: missed passages, outdated content, and incorrect data merging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

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