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Sandboxed proof pattern
Model lifecycle and private model lab proof pattern
This pattern shows how Folium can move model work out of guesswork and into a controlled lifecycle with candidate records, held-out tests, confidence gates, human review, and supportable release states.
Situation
A team is testing prompts, fine-tunes, local models, or commercial models without a clean way to compare quality, cost, safety, support, and release readiness.
Folium move
Build a model lab with candidate cards, scenario banks, evaluation datasets, prompt/version records, route tests, approval gates, and lifecycle states.
What gets tested
Accuracy, source support, latency, cost, refusal behavior, hallucination risk, prompt injection exposure, role fit, rollback, and operator comprehension.
What stays protected
Private datasets, weights, prompts, customer examples, private model names, and unreleased evaluation files remain scoped to approved environments.
Proof route
The pattern turns broad capability into reviewable operating steps.
Each lane keeps the same discipline: name the work, expose the route, test the boundary, package the record, and choose the next controlled move.
- 01 Inventory List model candidates, prompts, sources, costs, routes, and supported jobs.
- 02 Build cases Create representative, edge-case, failure, and regression examples.
- 03 Score candidates Compare model behavior against source support, risk, cost, latency, and user acceptance.
- 04 Gate release Promote, park, rollback, or retire candidates based on human-reviewed evidence.
- 05 Operate Track drift, incidents, new cases, prompt releases, model changes, and support owners.
Signals
What a reviewer should be able to see.
Promotion evidence
A model does not move forward because it feels impressive; it moves because it passes named checks.
Lifecycle clarity
Experimental, active, parked, retired, and rollback states are explicit.
Private lab boundary
The lab can prove method without exposing private model assets.
Public boundary
This pattern describes model lifecycle discipline. It is not proof of a private model's weights, private training data, customer results, or regulated approval.
Start here
Use the proof pattern to choose one controlled first move.
The broad capability surface stays visible, while the first build remains narrow enough to verify.