THE LICENSING FIELD GUIDE
AI dataset licensing guides and worksheets
Prepare a reviewable collection before discussing terms. Start with the four core documents, then check the details specific to your archive.
Every guide includes an editable outline you can copy or download without submitting an inquiry. These are preparation tools, not legal agreements or rights clearance.
Build your preparation package
Dataset source and rights inventory: a preparation template
Trace original, supplied, and third-party material to source records. Track permission questions and exclusions without treating a checklist as legal clearance.
AI data license scope: an editable worksheet
Map proposed training, retrieval, evaluation, users, duration, and downstream handling before legal review. Includes a worked example and worksheet.
Dataset evaluation plan: sample and acceptance checklist
Define a test question, sample selection, leakage checks, evidence, and a decision date before sharing a dataset for evaluation.
Dataset delivery manifest: template and handoff checks
Prepare a release manifest with file counts, schema, checksums, exclusions, and correction handling. Copy an editable delivery template.
Check the details of your archive
Technical manual licensing: revision and source checklist
Inventory manual revisions, supplied illustrations, OCR quality, and model applicability before proposing technical documents for AI use.
Inspection image licensing: labels, duplicates, and splits
Prepare inspection images with part-level counts, capture context, label provenance, and evaluation splits that expose coverage gaps.
Product catalog licensing: field sources and variant checks
Separate product text, specifications, and images by source; document units, variants, snapshot dates, and matching-test limitations.
Video archive licensing: clips, audio, and source inventory
Map recordings to clips, transcripts, audio sources, and review questions before preparing a video dataset sample or delivery.
Knowledge base licensing: versions, access, and source checks
Prepare support articles for AI evaluation by separating tickets, public and internal content, revisions, product applicability, and source gaps.