LLMOps & Production Realities
What happens after you ship
TL;DR
What happens after you ship
> Overview
There is a gap between we built an agent and it works reliably in production. This module covers the operational realities: prompt versioning, model provider management, cost monitoring, A/B testing agent behaviors, logging, observability, and the dreaded model update broke everything problem. This is the module that turns AI features from fragile demos into reliable production systems.
> Why This Matters for Your Product
Most AI products break within weeks of launch, not because the technology fails, but because the operational infrastructure was not built. A model provider updates their API and your prompts stop working. Your costs spike because a prompt change made the agent more verbose. A subtle quality regression goes undetected for two weeks. LLMOps is the discipline that prevents all of this. PMs need to understand it because they own the quality bar and the budget.
> Interactive & tools
LLMOps checklist
LLMOps readiness checklist
- Prompt versioning and registry
- Prompt tested against eval suite before deploy
- Gradual rollout and rollback for prompts
- Multi-provider strategy (secondary LLM ready)
- Per-feature cost dashboards with alerts
- Model version pinned in production
- Every agent interaction logged with traceability
- Regression tests for new model versions
Readiness: 0/8 (0%)
Cost dashboard (example)
Cost dashboard (example)
Set per-feature budgets and alerts so a prompt change or viral launch doesn’t surprise you.
Incident playbook
Related Engineering Patterns
These are the technical patterns your engineering team will implement. Understanding them helps you have better conversations.
Key Product Decisions
- [01]Do you have prompt versioning and a deployment pipeline?
- [02]What is your multi-provider strategy if your primary LLM goes down?
- [03]Do you have per-feature cost dashboards with alert thresholds?
- [04]How do you handle model updates that change agent behavior?
- [05]Is every agent interaction logged with full traceability?
Ask Your Engineering Team
- →Are we pinning model versions in production?
- →What is our prompt deployment and rollback process?
- →Do we have cost alerting set up per feature?
- →What is our observability stack for agent interactions?
- →How do we regression-test against new model versions?
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