Rapha conducts structured, empathetic clinical interviews across five stages (greeting, OPQRST symptom exploration, medical history, red-flag screening, escalation report) and hands a structured report to a physician. Rapha never diagnoses.

Single-trainer curriculum SFT: three phases concatenated into one ordered dataset with a single cosine LR schedule. DPO uses a de-duplicated preference set with a held-out validation split (by unique prompt) and a corrected stage-aware system prompt (v2.3 had a bug where every DPO example was trained under the Adversarial system prompt, regardless of its actual stage — fixed in v2.4).

Curriculum SFT across 6 datasets: Stage 1 greetings, Stage 2 OPQRST symptom exploration, Stage 3 medical history, Stage 4 red-flag screening, a multi-turn adversarial set (self-diagnosis, symptom denial, medication refusal, minimised red flags, prompt injection), and full-arc sessions ending in a Stage 5 handoff. Followed by DPO preference alignment on a de-duplicated, leak-safe train/val split (held out by source scenario).

Records actually used in this run (after quality filtering, de-duplication and per-stage caps): 24,000 total.

The interview question policy, red-flag screening, and the Suspicions for clinician consideration section in each full-arc report are generated by a deterministic forward-chaining rule engine (clinical_rules.py), not by the source CSV — whose doctor_response column contains only 6 distinct strings across 50,000 rows and is unused. Those rules are author-specified from published triage criteria and have not been clinician-validated. Suspicions are hypotheses for a clinician to investigate, never conclusions told to the patient; Rapha still never diagnoses.

Rapha is an information-gathering and triage-support tool. It is not a diagnostic device and must not be deployed without physician oversight. Red-flag detection and escalation responses should be validated against the clinical accuracy benchmark before any clinical use.