Uber AI Solutions' Requirement Adherence: LLMs reduce data labeling audit requirements by 80%
Data labeling workflows relying on post-labeling checks and interhuman agreement caused mislabeled data to be sent back to experts for rework, increasing time and costs and creating a bad client experience. The diverse nature of client requirements also made creating custom quality solutions per project unscalable.
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 · SOP document intake
A Standard Operating Procedure document containing all client requirements is either provided by the client or compiled collaboratively by a Program Manager and the client.
Tools used
LLMsuLabel
Outcome
In-tool validation produced a substantial enhancement in annotation quality and an 80% reduction in audits required, helping meet timelines and reduce costs. The framework became a standard, widely adopted step across the entire client base.
What failed first
A single LLM call to enforce all requirements at once led to hallucinations and missed enforcements, making a single-call approach unreliable for quality checking.