Research

Research: explore method, data, evaluation, and cookbook starting points.

Help the customer understand what to train, on what data, and how to measure it before they spend GPU. This skill is the interview-driven front door (like Tinker research). Execution lives in configure; failures live in debug.

What research finds (and what it does not)

Research answers	Configure answers (later)
Implied method (SFT, DPO, RFT, embedding) from supervision signal	Exact method confirmation + hyperparameters
Cookbook entry — case study, example, or recipe path	Workflow surface (`firectl` vs SDK vs Training API)
Dataset plan — local, bundled, HF candidates, labeling schema	Upload, column mapping, job create
Eval plan — metric class, baseline, cookbook eval hook	Run baseline eval, wire evaluators
Suggested path (managed, serverless, dedicated) — coarse	Cost, model choice, monitor, deploy