Course Objective & Scope
In a live AI product, a one-line prompt tweak, an updated tool schema or a forced foundation-model deprecation can trigger silent behaviour shifts and customer-visible regressions. This course teaches how to bring software-grade change management and release governance to prompts, models and tools.
Participants design prompt and tool-schema versioning workflows, model migration playbooks, automated regression eval gates, canary rollouts and instant rollback mechanisms, producing a complete change management and release checklist for their organisation.
Who Should Attend?
- AI product managers (AI PMs) and technical programme managers operating live AI features
- LLMOps, platform and applied AI engineers
- Quality assurance (QA) and release engineering teams
- Prerequisite: experience building or managing a production LLM or agent application
Key Takeaways
- Version prompts, model configs and tool schemas as immutable, auditable artifacts
- Catch prompt drift and behavioural regressions automatically before changes reach production
- Execute structured model migration playbooks when upgrading or switching model providers
- Maintain backward compatibility when evolving tool and MCP schemas across active agent sessions
- Implement feature-flagged canary rollouts and zero-downtime config rollbacks
Curriculum
Why Minor Prompt or Model Changes Break Production
- Prompt–model entanglement and silent behavioural drift
- Model version pinning, provider deprecation timelines and configuration inventories
Versioning Prompts, Model Configs and Tool Schemas
- Managing a prompt registry (Langfuse, Braintrust, GitOps) across dev, staging and prod
- Backward-compatible evolution of tool calling and MCP schemas
The Foundation Model Migration Playbook
- Re-tuning prompts and system instructions when upgrading or switching model families
- Testing for shifts in structured output formatting, verbosity, tone and tool-call eagerness
Automated Regression Gates and Canary Rollouts
- Mandatory accuracy, latency, cost and safety eval gates on every config change
- Progressive traffic ramping (1% → 10% → 50%) and instant feature-flag rollback
Release Governance, Changelogs and Incident Response
- Cross-functional sign-off workflows across PMs, domain experts and engineering
- Delivering a model/prompt/tool change management protocol and release checklist
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