Core Concepts
Users face cognitive challenges in formulating clear intentions and prompts for LLMs, leading to the "Gulf of Envisioning."
Abstract
The content discusses the cognitive challenges users face when interacting with Large Language Models (LLMs) like ChatGPT. It explores the concept of envisioning intentions and how users struggle with planning, executing, and evaluating their interactions with LLMs. Three key gaps are identified: capability gap, instruction gap, and intentionality gap. The analysis is based on three interfaces: ChatGPT for writing tasks, Spellburst for creative coding, and Cursor for text editing.
ChatGPT:
Provides example prompts but lacks granularity in task breakdown.
Users discover how LLM interprets prompts through trial and error.
Custom instructions help align user values with model output.
Spellburst:
Offers example sketches and autocomplete suggestions.
Provides semantic operators for extending current ideas.
Allows comments in code output for evaluation.
Cursor:
Highlights text suggestions and provides code explanations.
Supports referencing external resources for better understanding.