Guides
Build a specialist research workflow
A specialist workflow begins with one repeated task whose expected outputs and failure modes can be inspected. SAVRN Cloud links sample datasets, environments and baselines to help explain when prompt changes, tools or model adaptation would merit further testing.
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Start with one repeated task
Choose a narrow synthetic task, such as extracting instrument names from fictional lab notes. Create or inspect the supporting data in Datasets, describe the scoring approach in Environments, then run a baseline in Evaluations.
Use the same task revision when comparing demonstration models or workflow changes. Review per-task expectations before interpreting an aggregate score. All local results are synthetic, so the purpose is to inspect workflow completeness rather than select a real model.
Add complexity deliberately
Improve the task instructions or rubric first. If the application walkthrough calls for specialization, inspect Training, follow its candidate in Candidates and review the local promotion path. A training record and a deployed candidate remain separate artifacts.
Use a Work Order when the specialist workflow needs a human approval and an accepted deliverable.
Before connected services launch
Establish actual task quality and tool boundaries before adding autonomy. Validate the reward or rubric against held-out cases and known failure modes. Training requires approved data and a supported recipe, not just an available GPU. Release only after quality, cost, latency, isolation and recovery checks pass. Keep the original baseline and rollback target so an apparent improvement can be re-examined after real users encounter new cases.
Build toward the work that matters.
Tell SAVRN what your institution needs to run, who reviews the results and where its data must stay. That workload defines the next service to qualify.
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