Training data, human feedback, evaluation workflows and reviewable quality evidence for AI systems.
AI Data & Evaluation
Compare workflow versions under recorded conditions, separate critical failures from review warnings and keep exceptions attached to the release decision.
Preserve the prompt, alternatives, comparison criteria and uncertainty so a chosen response is not mistaken for an independently correct answer.
Define batch acceptance around label rules, targeted review, error categories and correction ownership rather than one agreement number.
Keep origin, permitted use, transformation history and dataset versions connected without turning provenance into unnecessary sensitive-data retention.
Turn broad quality goals into observable criteria, boundary examples and versioned decisions without hiding uncertainty inside one score.
Separate source availability, retrieval relevance and answer support so a RAG evaluation result points to the next engineering decision.
Choose representative tasks, failure slices and a held-out set before using an AI evaluation score to approve a change.
An independent analysis of OpenAI's published Morgan Stanley account: task-specific evaluation, expert feedback and reviewable meeting summaries. Not XIVTech client work.
Separate unclear criteria from genuine borderline cases, then use anchor examples and adjudication to make human AI feedback more useful.
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