Deploy models that are accountable in the real world

RLHF (Reinforcement Learning from Human Feedback) preference data is human judgements comparing multiple model outputs, ranking them on quality, accuracy, safety, and instruction adherence. Firstsource runs preference ranking programs with domain-matched human evaluators, using IAA scoring and calibration passes to ensure inter-rater consistency throughout. The resulting preference dataset is used to train a reward model that guides the language model toward preferred outputs during reinforcement learning.

Fine-tuning data (SFT and CoT) teaches the model what to do by showing it expert examples. Post-training data teaches the model what humans prefer by ranking outputs and providing feedback on safety, alignment, and quality. Post-training also includes regression sets to prevent capability degradation, safety adversarial corpora for red teaming, and human-in-the-loop feedback loops. Firstsource delivers both phases under a unified operating model, with no vendor switch at the alignment stage.

A regression set is a curated battery of tasks and prompts that a model must pass before and after each post-training update, to confirm that alignment improvements did not degrade capability elsewhere. Without regression testing, safety tuning routinely causes capability regressions: a model that becomes safer on harmful prompts may also become less accurate on legitimate domain tasks. Firstsource designs regression sets specific to your model’s domain, benchmark targets, and safety requirements.

A clinical AI post-training program includes: specialty-matched physician evaluators for preference ranking across relevant clinical knowledge layers, IAA scoring calibrated to clinical best-practice standards, hallucination regression sets designed around the model’s specific clinical tasks, and HIPAA §1557-aligned data handling throughout. As demonstrated on a 14-hospital clinical AI readiness engagement, this approach moved clinical plausibility from 61% to 83.7%. A program lead will walk through scope, timeline, and clinical domain coverage during scoping.

Firstsource runs structured red-teaming programs using a specialist adversarial team, discovering jailbreaks, hallucination triggers, policy bypasses, and harmful output patterns before they reach production. Red-teaming outputs feed directly into the safety alignment corpus: each discovered failure mode generates adversarial examples for the safety fine-tuning and RLHF negative-example sets. The program scope, domain focus, and adversarial categories are defined in the Xplore scoping phase before collection begins.