Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts

Pre-trained large language models (“LLMs”) like GPT-3 can engage in fluent, multi-turn instruction-taking out-of-the-box, making them attractive materials for designing natural language interactions. Expectations stemming from human-to-human instructional experiences, and a tendency to overgeneralize, were barriers to effective prompt design.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Using natural language to steer LLM outputs (“prompting”) has emerged as an important design technique potentially accessible to non-AI-experts.
• 2 Crafting effective prompts can be challenging, however, and prompt-based interactions are brittle.
• 3 Here, we explore whether non-AI-experts can successfully engage in “end-user prompt engineering” using a design probe—a prototype LLM-based chatbot design tool supporting development and systematic evaluation of prompting strategies.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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