Prompt and Policy Management as Production Assets
Overview
Prompts drift. Policies conflict. Production AI needs change control like any critical system.
Quick definition
Prompt policy management versions prompts as artifacts with semver, ties them to model IDs and temperature bounds, and requires review before promotion—like infrastructure config.
Definition
Prompt/policy management tracks versions, authors, environments (dev/stage/prod), and links changes to measured outcomes.
Why it matters
Ad-hoc prompt edits in production are undebuggable and unauditable.
Core framework
Git-like workflows
PR reviews for prompt changes; CI evals on golden sets.
Feature flags
Gradual rollout of new templates by segment.
Detailed breakdown
Separation of concerns
Business policy in config; linguistic style in prompt layers.
Technical patterns
Prompt registry
- `prompt_id@version` stored in git or config service; runtime resolves active.
- Eval harness scores each candidate on golden sets before prod.
Code examples
Resolve active prompt
Lookup with safe default.
export async function getPrompt(name) {
const rec = await registry.getActive(name);
if (!rec) throw new Error(`missing prompt ${name}`);
return rec.text;
}System architecture
[Authoring + PR review]
→ [Registry: versioned prompts]
→ [Eval CI gate]
→ [Runtime resolver]
→ [Telemetry: prompt version in traces]Real-world example
A support org reverted a harmful prompt in minutes using versioned templates—restoring CSAT.
Common mistakes
- Editing live prompts without tests.
- No ownership—everyone edits, no one accountable.
Related topics
PrimeAxiom implements governance for generative workflows—book a prompt ops review.