Agentic operating layer prompt suite
Modular prompt library with persistent memory hooks, injection-defense policies, and a continuous-eval loop.
SVC // 06
AI Prompt Engineering
Prompts are a public API. We design them deliberately — with eval harnesses, drift detection, redaction policies, and a registry that survives model upgrades and team turnover.
▌▌▌ WHAT WE DELIVER ▐▐▐
DELIVERABLES
Prompts treated like code.
- // PROMPT REGISTRY Versioned prompts with provenance, owners, and changelogs — not a Notion doc.
- // EVAL HARNESS Deterministic suites, golden cases, and human-rated samples to catch regressions before users do.
- // A/B HARNESS Side-by-side runs across models, temperatures, and prompt variants with statistical confidence.
- // DRIFT DETECTION Production sampling that alerts when behavior changes — model update or otherwise.
- // REDACTION POLICY Pre- and post-flight filters for PII, secrets, and disallowed outputs.
- // RUNBOOKS What to do when the model misbehaves at 2am.
▌▌▌ REPRESENTATIVE ENGAGEMENTS ▐▐▐
DOSSIER
Selected work — redacted.
▌▌▌ HOW WE WORK ▐▐▐
PROCESS
Spec. Eval. Lock.
- // 01 SPEC Write what the prompt is supposed to do — for users, for adversaries, for compliance.
- // 02 EVAL Build the suite. Score baseline. Iterate against the suite, not against vibes.
- // 03 LOCK Version. Ship. Sample production. Alert on drift.