Community Facilitator Guide
GenAIOps for Cloud Solution Architects: From Prompt to Production — a guide for running community lunch-and-learns, lightning talks, and workshops.
Audience
- New CSA starters who need a simple way to explain GenAIOps from prompt experiments to production readiness.
- Early-career cloud architects building confidence in Azure AI architecture, governance, and operational thinking.
- Community learners exploring how GenAI changes architecture, evaluation, and monitoring practices.
- Technical consultants who need a reusable public-safe session format for internal or community events.
Duration options
| Format | Duration | Shape |
|---|---|---|
| Lightning talk | 15 minutes | Lifecycle overview and three lessons learned. |
| Lunch and learn | 45 minutes | Lifecycle, reference architecture, use cases, Q&A. |
| Workshop | 90 minutes | Lifecycle, group exercise, evaluation checklist, operating model. |
Learning outcomes
- Explain GenAIOps in plain language.
- Describe the Build, Evaluate, Deploy, Monitor, Govern, Optimise lifecycle.
- Identify why evaluation and monitoring are different for GenAI.
- Explain where an AI gateway helps.
- Ask better customer discovery questions.
Suggested agenda for a 45 minute session
| Time | Activity |
|---|---|
| 0-5 | Why GenAI demos are not the same as production services. |
| 5-15 | GenAIOps lifecycle walkthrough. |
| 15-25 | Reference architecture: app, gateway, agent, retrieval, model, safety, telemetry. |
| 25-35 | Use case exercise: choose a pilot and define controls. |
| 35-42 | Common questions and objections. |
| 42-45 | Takeaways and next steps. |
Group exercise
Ask attendees to choose one realistic pilot use case, then work through the operational questions that separate a demo from a production service.
- Who is the user?
- What task are they trying to complete?
- Which data sources are trusted?
- What could go wrong?
- How would you evaluate quality before release?
- What would you monitor after release?
- Who owns the solution in production?
Safe public framing
Keep the examples public-safe. Avoid customer-specific scenarios and use generic examples such as support knowledge assistants, service desk triage, contact centre summarisation, policy assistants, and engineering runbook assistants.