  Text entry is an essential task in our day-to-day digital interactions.
Numerous intelligent features have been developed to streamline this process,
making text entry more effective, efficient, and fluid. These improvements
include sentence prediction and user personalization. However, as deep
learning-based language models become the norm for these advanced features, the
necessity for data collection and model fine-tuning increases. These challenges
can be mitigated by harnessing the in-context learning capability of large
language models such as GPT-3.5. This unique feature allows the language model
to acquire new skills through prompts, eliminating the need for data collection
and fine-tuning. Consequently, large language models can learn various text
prediction techniques. We initially showed that, for a sentence prediction
task, merely prompting GPT-3.5 surpassed a GPT-2 backed system and is
comparable with a fine-tuned GPT-3.5 model, with the latter two methods
requiring costly data collection, fine-tuning and post-processing. However, the
task of prompting large language models to specialize in specific text
prediction tasks can be challenging, particularly for designers without
expertise in prompt engineering. To address this, we introduce Promptor, a
conversational prompt generation agent designed to engage proactively with
designers. Promptor can automatically generate complex prompts tailored to meet
specific needs, thus offering a solution to this challenge. We conducted a user
study involving 24 participants creating prompts for three intelligent text
entry tasks, half of the participants used Promptor while the other half
designed prompts themselves. The results show that Promptor-designed prompts
result in a 35% increase in similarity and 22% in coherence over those by
designers.
