Use case
Response generation
Develop a set of truthful, relevant, and reasonable answers to key prompts to better train your LLM and AI assistant
Why Labelbox for response generation
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 response generation
Creating high-quality prompt/response pairs offline
Develop valuable responses for training by providing labelers with a standard question a user might ask your AI assistant and capturing the appropriate response to the prompt. Evaluate the quality of the responses based on accuracy, relevance, safety, grammar, verbosity, and more.
Simplifying project management and app integration
Guide internal and external labelers through custom workflows to generate responses offline to a set of imported prompts. Integrate full end-to-end pipeline for training data creation with seamless cloud integration and synchronization as well as API integration to downstream applications for LLM training building.
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.
