In traditional software, code goes through a pipeline: write, review, test, merge, deploy, monitor. When AI agents write the code, the input that drives them — the prompt — becomes the most important artifact. Yet most teams treat prompts as throwaway chat messages.
PromptOps changes that. It is the discipline of operationalizing prompts as first-class production assets: shared, versioned, routed, audited, and gated before anything reaches production.
PromptOps vs. MLOps / LLMOps
MLOps
Focuses on training, serving, and monitoring machine-learning models.
LLMOps
Adds prompt engineering, evals, and observability on top of large language models.
PromptOps
Treats prompts as team-owned, versioned, reversible assets inside a delivery pipeline.
The four pillars of PromptOps in Orquesta
1. Version & collaborate
Prompts live in a shared workspace. Anyone can propose changes, comment, re-run previous versions, and roll back when something breaks.
2. Smart routing
Every prompt is classified by complexity, cost, and required capabilities, then routed to the right model tier — fast, balanced, or premium.
3. Audit trail
Who wrote the prompt, which model ran it, what changed, and when. Every execution is tied to a git commit and a team member.
4. Quality gates
AI proposes changes; humans approve them. Require sign-off for sensitive paths, or whitelist safe patterns that can merge automatically.
Ready to adopt PromptOps?
Set up Orquesta in 90 seconds and give your team a PromptOps workflow from prompt to production.