  Predictive suggestion systems offer contextually-relevant text entry
completions. Existing approaches, like autofill, often excel in
narrowly-defined domains but fail to generalize to arbitrary workflows. We
introduce a conceptual framework to analyze the compound demands of a
particular suggestion context, yielding unique opportunities for large language
models (LLMs) to infer suggestions for a wide range of domain-agnostic
form-filling tasks that were out of reach with prior approaches. We explore
these opportunities in OmniFill, a prototype that collects multi-faceted
context including browsing and text entry activity to construct an LLM prompt
that offers suggestions in situ for arbitrary structured text entry interfaces.
Through a user study with 18 participants, we found that OmniFill offered
valuable suggestions and we identified four themes that characterize users'
behavior and attitudes: an "opportunistic scrapbooking" approach; a trust
placed in the system; value in partial success; and a need for visibility into
prompt context.
