quality_assurance · media · workflow
Leading genAI text-to-image app accelerates product development with Labelbox high-quality labeled data
The company needed to rapidly generate high-quality human preference training data for its text-to-image AI models, but labeling data internally would have diverted resources from core product development. The complexity and labor-intensive nature of generative AI data labeling required dedicated tooling and teams to meet tight timeframes.
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 was looking for rapid ways to generate high-quality human preference training data.
Tools used
LabelboxLabelbox Labeling ServicesLLM human preference editormultimodal chat solution
Outcome
Using Labelbox, the company improved the speed of creating human preference training data by 2x and accelerated product development from months to weeks.
Results
Time savedmonths to weeks
Volume2x
Grounding & classification
Source type: vendor customer story
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