What’s new: What’s new: Taskflow updates, quota sampling by age, and more!
Welcome to our latest product update! We’ve released several enhancements to our platform that make AI evaluations and research much easier on Prolific.
Read on to find out what’s new, including updates on Taskflow, a new feature that lets you run quota sampling by age groups, and UI improvements.
Taskflow performance upgrades
We've made several enhancements to Taskflow, so you can collect annotations of larger datasets faster than before.
Taskflow, a Prolific feature, breaks down your annotation datasets into smaller tasks. Multiple participants can then complete these at the same time. Our URL-based distribution system lets you precisely batch tasks across conditions and get your data faster.
With our latest updates to Taskflow, now you can:
- Create unlimited unique URLs with specific annotator allocations for each task
- Automatically reallocate tasks if participants don't complete their assignments
- Dynamically add batches while maintaining even distribution
Taskflow powers all kinds of large-scale annotation projects, from emotion labeling and preference testing to training data creation for AI systems. Learn more about using Taskflow in our help center.
Quota sampling by age range is here
We've expanded our demographic targeting capabilities with age range filtering in quota studies:
- Set specific participant quotas by age brackets
- Ensure balanced representation across demographic segments
- Reduce participant drop-off by targeting precise age groups
Currently, age range quota sampling is available through our API only. View implementation examples in our API documentation.
Editable participant groups
Sometimes, small improvements make a big difference in your daily workflow. Participant group names are now fully editable via the UI. Now you can organize your data collection projects with clear, consistent naming conventions.
Featured resources
How Prolific guarantees data quality
Discover how we verify participants and prevent LLM-generated responses in research, so your AI models learn from real human data—not artificial patterns. Download our whitepaper here.
What does the public think about AI policy?
Explore our report comparing US and UK approaches to AI governance, public understanding of regulations, and where policy development is heading.
A practical guide to building AI apps with LLMs
Learn how to choose the right models, find labelers for fine-tuning, and build useful and reliable AI products. Get your guide here.
