{"id":688,"date":"2026-09-16T08:33:31","date_gmt":"2026-09-16T08:33:31","guid":{"rendered":"https:\/\/perit.ai\/blogs\/a-practical-guide-to-requesting-custom-training-data\/"},"modified":"2026-09-16T14:33:32","modified_gmt":"2026-09-16T14:33:32","slug":"a-practical-guide-to-requesting-custom-training-data","status":"publish","type":"post","link":"https:\/\/perit.ai\/blogs\/a-practical-guide-to-requesting-custom-training-data\/","title":{"rendered":"A Practical Guide to Requesting Custom Training Data"},"content":{"rendered":"

The single biggest predictor of a successful data collection engagement is how precisely the request is scoped before collection ever starts.<\/p>\n

Start with the failure case<\/h2>\n

Rather than describing the data you want in the abstract, describe the failure you’re trying to fix \u2014 the exact prompt, accent, or scenario your current model handles badly.<\/p>\n

Define the spec<\/h3>\n

Modality (audio, video, image, text), language and accent coverage, recording environment, and any device constraints all belong in the spec document before outreach to contributors begins.<\/p>\n

Set the grading rubric up front<\/h3>\n

Decide what \u201cgood\u201d looks like before the first clip comes in \u2014 it\u2019s much harder to retroactively agree on quality standards once a dataset is halfway collected.<\/p>\n

Scale and timeline<\/h2>\n

Larger, narrower asks (a specific accent, a specific task) usually move faster than broad, loosely-specified ones \u2014 precision in the spec is what makes collection scale predictably.<\/p>\n

How to choose a collection partner<\/h2>\n

Ask for a sample batch against your rubric before committing to full scale \u2014 it’s the fastest way to confirm a vendor’s quality bar actually matches yours.<\/p>\n","protected":false},"excerpt":{"rendered":"

Everything a data or ML team should decide before submitting a data collection request \u2014 spec, scale, and grading criteria.<\/p>\n","protected":false},"author":1,"featured_media":730,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-688","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-guides"],"_links":{"self":[{"href":"https:\/\/perit.ai\/blogs\/wp-json\/wp\/v2\/posts\/688","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/perit.ai\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/perit.ai\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/perit.ai\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/perit.ai\/blogs\/wp-json\/wp\/v2\/comments?post=688"}],"version-history":[{"count":1,"href":"https:\/\/perit.ai\/blogs\/wp-json\/wp\/v2\/posts\/688\/revisions"}],"predecessor-version":[{"id":731,"href":"https:\/\/perit.ai\/blogs\/wp-json\/wp\/v2\/posts\/688\/revisions\/731"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/perit.ai\/blogs\/wp-json\/wp\/v2\/media\/730"}],"wp:attachment":[{"href":"https:\/\/perit.ai\/blogs\/wp-json\/wp\/v2\/media?parent=688"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/perit.ai\/blogs\/wp-json\/wp\/v2\/categories?post=688"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/perit.ai\/blogs\/wp-json\/wp\/v2\/tags?post=688"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}