Authoring LLM-Based Assistance for Real-World Contexts and Tasks

DOI

Advances in AI hold the possibility of assisting users with highly varied and individual needs, but the breadth of assistance that these systems could provide creates a challenge for how users specify their goals to the system. To support the authoring of AI assistance for real-world tasks, we propose the concept of Contextually-Driven Prompts (CDPs) that define how an AI assistant should respond to real-world context. We implemented a prototype system for authoring and executing CDPs, which provides suggestions to assist users with finding the right level of assistance for their goal. We also conducted a user study (N=10) to investigate how participants express and refine their goals for real-world tasks. Results revealed a number of strategies for initiating and refining CDPs with suggestions, and implications for the design of future authoring interfaces.