Use case
Preference ranking
Test AI agents by interacting with a trained assistant to test the functionality and perform red teaming
Why Labelbox for preference ranking
Increase data quality
Generate high-quality data by combining advanced tooling, humans, AI and on-demand services in a unified solution.
Accelerate time to value
Rapidly integrate data, create quality training data, and deploy models to production.
Access on-demand expertise
Highly-skilled labeling services, data science support, and industry insights available on-demand.
Collaborate in real-time
Enjoy direct access to internal and external labelers with real-time feedback on labels and quality via Labelbox platform.
Explore models for preference ranking
Evaluating the responses from trained AI agents
Use Labelbox’s interface and customized workflows to analyze the outputs from an AI assistant. Record findings across a variety of responses and present the findings back to improve the performance of the models.
Customized workflows for powerful red teaming
Design the best workflow for your needs using the Labelbox LLM human preference editor, which allows users to rank or classify different model responses to a given prompt and provide correction, description and/or justification towards their selections to effectively evaluate model functionality.
Accelerating development with on-demand services
Access an on-demand, highly skilled data labeling service with subject matter expertise to match your use cases. Collaborate with the workforce in real-time to maintain high data quality while keeping human labeling costs to a minimum using AI and automation techniques.
Customer spotlight
Dialpad, a leading AI-powered customer intelligence platform company used fine-tuning to build a powerful LLM over five years via five billion minutes of business conversations. The model offers out of the box capabilities to businesses to accurately summarize business calls, extract important insights and offer in-the-moment coaching to sellers and customer reps. Advanced discovery, curation, and annotation capabilities were crucial for building high-quality training datasets. Human evaluation was also critical to ensure quality outcomes before the system was turned live. Labelbox enabled this organization to accelerate the creation of the LLM by 75% through rich, integrated capabilities for data preparation and human evaluation.
