AI Task Builder provides two distinct approaches for human data work: Batches for annotating and evaluating existing data, and Collections for gathering original data from participants.
Both approaches integrate with Prolific’s participant pool and share core functionality like instruction types and quality controls, but they’re designed for fundamentally different workflows.
The key difference comes down to the direction of data flow:
Use an AI Task Builder Batch when you have existing data that needs human judgement. Typical use cases include:
With Batches, you upload a dataset (typically via CSV), and AI Task Builder distributes datapoints across participants via Taskflow. Each participant sees a subset of your data, and you can configure how many annotators evaluate each datapoint.
Use an AI Task Builder Collection when you need participants to provide original content. Typical use cases include:
With Collections, you define pages containing instructions and optional reference content. All participants complete the same flow, submitting their responses and uploads as they progress through each page.
Both Batches and Collections share some common building blocks:
Instructions define what you’re asking participants to do. Available instruction types include:
Both Batches and Collections are attached to a Prolific study for publishing. When creating the study, you specify:
data_collection_method: Either AI_TASK_BUILDER_BATCH or AI_TASK_BUILDER_COLLECTIONdata_collection_id: The ID of your Batch or CollectionLearn the workflow for setting up annotation tasks with your own datasets
Learn the workflow for gathering original data from participants
By using AI Task Builder, you agree to our AI Task Builder Terms.