  Quality control in crowdsourcing systems is crucial. It is typically done
after data collection, often using additional crowdsourced tasks to assess and
improve the quality. These post-hoc methods can easily add cost and latency to
the acquisition process--particularly if collecting high-quality data is
important. In this paper, we argue for pre-hoc interface optimizations based on
feedback that helps workers improve data quality before it is submitted and is
well suited to complement post-hoc techniques. We propose the Precog system
that explicitly supports such interface optimizations for common integrity
constraints as well as more ambiguous text acquisition tasks where quality is
ill-defined. We then develop the Segment-Predict-Explain pattern for detecting
low-quality text segments and generating prescriptive explanations to help the
worker improve their text input. Our unique combination of segmentation and
prescriptive explanation are necessary for Precog to collect 2x more
high-quality text data than non-Precog approaches on two real domains.
