GenAI for Practitioners

Module 1: Prompting that scales across tools

• Role, goal, inputs, constraints, and output format as a template
• Using examples, rubrics, and style guides to stabilize tone
• Iterating with targeted follow ups and preserving context

Module 2: Tool landscape and fit for purpose

• Assistant tools in practice such as ChatGPT, Claude, Gemini, Copilot
• Platform choices such as OpenAI API, Azure OpenAI, Vertex AI, Bedrock, and on prem open source
• Selection criteria such as capabilities, cost, latency, data controls, and extensibility

Module 3: Grounding with your content

• Retrieval basics and packaging context for reliable answers
• Citing sources, suggesting links, and handling unknowns
• Small patterns for FAQs, search plus answer, and brief generation

Module 4: Using multiple tools together

• When to switch tools or chain them for strengths
• File handling, images, and simple tables across tools
• Lightweight orchestration ideas and guardrails for handoffs

Module 5: Quality and evaluation

• Designing acceptance criteria for accuracy, completeness, and tone
• Spot checks, golden examples, and lightweight scorecards
• Tracking edits and measuring improvement over time

Module 6: Safety and governance basics

• Handling sensitive data and redaction prompts
• Bias and hallucination reduction with check prompts and citations
• Version notes, decision logs, and sharing templates across teams

Module 7: Small end to end workflows

• Email or brief or report pipelines with reusable prompts
• RAG style support answers with citations and follow ups
• Handing off to spreadsheets, docs, or ticketing tools

Module 8: Choosing your stack and next steps

• Tool shortlist matched to your use cases and constraints
• Cost and performance rough order estimates and simple dashboards
• Personal action plan and a shared template library