quality_assurance · saas · workflow
Labelbox assembles 150 STEM experts to evaluate and improve a leading AI lab's multimodal LLM
A leading AI lab needed a reliable team of qualified STEM experts to evaluate their LLM on K-12 domain-specific questions and generate multimodal training data, but faced difficulty sourcing specialists with deep technical expertise across biology, physics, engineering, and related fields.
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 · AI lab identifies evaluation need
A leading AI lab sought to identify areas within K-12 STEM education where their LLM struggled to generate accurate responses.
Tools used
LabelboxAlignerrmultimodal chat editor
Outcome
Labelbox's team of STEM experts consistently generated unique multimodal reasoning prompts that identified the model's limitations and significantly enhanced its performance on complex STEM questions, with Labelbox now serving as a fully integrated partner in the lab's real-time loss training workflow.
Results
Time saved24-hour calibration period
Volume150
Grounding & classification
Source type: vendor customer story
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