Leverage the power of LLMs to generate high quality datasets using our Prompt interface. Use natural language to engineer the ideal prompt. Then use that prompt to enable your model to accurately label your entire dataset.
Reliably automate data labeling using our purpose-built UI and constrained generation to prevent hallucinations. Keep inference costs down by measuring prompt performance against a ground truth dataset to ensure the LLM generates accurate labels at scale.
We experimented with ChatGPT to bootstrap labeling, but experienced many hallucinations.
Using Label Studio, we achieved 93% human labeling accuracy through automation, and seamlessly integrated the predictions into our quality review workflow.
Tilo Sperling, Head of AI-Projects Business Applications at Geberit
Rapidly create and improve prompts using real-time feedback to ensure label accuracy. Leverage prompt versioning to catalog and implement your best-performing prompts to efficiently label your large datasets.
Use ground truth data to fine-tune a prompt for auto-labeling large-scale datasets, get real-time quality feedback, and automatically convert predictions into a labeling project.
Don’t have ground truth data? Use Prompts with LLMs to bootstrap predictions, which you can convert to annotations, or review and analyze in a labeling project.
Evaluate your prompt against the ground truth annotations to generate an accuracy score for each version. Use this to iterate your prompt versions for clarity, specificity, and context.
Want to learn more about Prompts? Schedule some time to talk to one of our experts!