Insights on expert networks, market research, UX research, and AI training from the CleverX team.
154 articles
In specialized domains a hallucination is a confident, plausible answer that is wrong, and only a practitioner reliably catches it. This guide covers how expert review, rubrics, and benchmarks measure and reduce domain hallucinations.
Expert data costs more per unit than crowd labeling and is worth it where a wrong answer is expensive. This breaks down the pricing models, the ROI math, and how to build a budget that survives finance review.
Public benchmarks cannot tell you whether a model is good at your domain. This is how to construct a domain-specific evaluation set with verified professionals, from coverage mapping to contamination control.
A worked example is only as good as the person who wrote it and the process that captured it. This is the operational playbook for collecting expert demonstrations at quality, from task design to calibration.
Data annotation and expert evaluation solve different problems: one labels inputs at volume, the other judges outputs with professional reasoning. This guide explains both and how to combine them.
A rubric is only as good as the design process that produced it. This is how to run that process with domain experts, from eliciting tacit criteria to calibrating graders until they agree.
AI agents fail across whole workflows, not single answers, so testing them means scoring the full trajectory with domain experts. This guide is a practical playbook for evaluating multi-step agents in real-world domains.
In regulated industries, AI evaluation must be done by licensed professionals against documented standards, with audit trails. This guide lays out an expert-led approach for healthcare, finance, and legal.
AI labs buy expert data by deliverable, not in bulk, and the hard part is pricing specialized judgment and controlling its quality. This guide walks through procurement, pricing, and quality control end to end.
Human-in-the-loop keeps a qualified person in the decision path so AI errors get caught before they reach users. This guide covers when to use it, where to place the checkpoint, and how to staff review with verified experts.
Subject-matter expert sourcing is how AI teams find, verify, and retain the practicing professionals who label, evaluate, and demonstrate for specialist models. This guide covers scoping, verification, pricing, and scaling.
Vertical AI is only trustworthy when verified practitioners produce the data that trains and tests it. This guide covers what healthcare, finance, and legal AI need from expert data and how to source it.