quality_assurance · media · workflow
Leading genAI text-to-image app accelerates human preference training data creation with Labelbox RLHF tooling
A leading generative AI text-to-image app needed to rapidly generate high-quality human preference training data to improve its models, but creating and labeling this data internally would divert valuable resources from core product development and required dedicated tooling and labeling teams to meet tight deadlines.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Training data need identified
The company sought rapid ways to generate high-quality human preference training data for its text-to-image AI models.
Tools used
LabelboxLabelbox Labeling ServicesLLM human preference editormultimodal chat solution
Outcome
Using Labelbox, the company significantly improved the speed at which it could create high-quality human preference training data and sped up product development from months to weeks.
Results
Time savedfrom months to weeks
Volume2x
Grounding & classification
Source type: vendor customer story
22 fields verified against source quotes.
quality inspectionhuman review describedmetric backedproduction runtime claimedtools describedvendor confirmedworkflow describedmediasoftwarecycle time reductionthroughput increasetime savedvendor customer storydata entry opsquality assurancehuman review queue