Prompting, Auto-Prompting, and Human-AI Communication

We discuss the importance of prompts in human-Large Language Model (LLM) interaction, describing them as the primary interface through which users communicate intent and guide model behavior. However, they also critically examine the limitations of this prompt-centric approach, highlighting issues like LLM hallucinations, bias, reasoning struggles, and the "brittleness" of prompts that makes interaction unreliable. The papers explore the rise of auto-prompting as an attempt to automate prompt creation, noting both its potential benefits like performance improvement and its own challenges, such as computational cost and persistence of brittleness. Ultimately, the texts argue that while prompts are currently central, the future of AI communication will likely move beyond simple prompting towards more complex, systemic approaches that consider factors like training data, model architecture, user psychology, and the development of agentic systems for more sophisticated human-AI collaboration.