Insights on expert networks, market research, UX research, and AI training from the CleverX team.
175 articles
Alignment is usually treated as a generalist labeling problem, but the failures that matter most on specialized tasks are the ones only a practitioner can see. This maps where expert judgment belongs in the safety stack.
Code models pass unit tests and still ship insecure, unmaintainable, or subtly wrong code. This guide covers how expert evaluations, rubrics, benchmarks, and demonstrations from working engineers raise code-model and agent quality.
Scientific AI fails in ways only a working scientist can see. This is how to evaluate chemistry, biology, and engineering models with verified domain experts, from rubrics to benchmarks.
A customer-facing agent can be fluent and still get a customer in trouble. This is how to evaluate support, sales, and service bots with the professionals who do the job.
Fine-tuning, RAG, and expert feedback solve different accuracy problems, and teams waste months using the wrong one. This guide maps each to the failure it fixes and shows why all three rely on verified expert data.
Expert data is only as good as its QA. This playbook covers inter-rater reliability, gold sets, and the quality controls that make expert evaluations defensible.
Multimodal models produce images, audio, and video that look right and are often wrong in ways only a practitioner can catch. This guide covers how expert evaluations, rubrics, benchmarks, and demonstrations raise multimodal quality.
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.