7 stories tagged Prompt Engineering
Researchers have developed TAPR (Task-Aware Prompt Rewriter), a system that automatically rewrites user prompts to improve the performance of large language models (LLMs) like ChatGPT. Trained with reinforcement learning, TAPR aims to make AI more accessible to non-experts by eliminating the need for manual prompt engineering.
Researchers from ArXiv cs.CL have developed a system that automatically evolves prompt guidelines tailored to specific tasks, addressing the common problem of underspecified user queries. This could make large language models like ChatGPT and Claude more accurate and user-friendly without requiring prompt engineering expertise.
Researchers introduced Contrastive Reflection, a method that helps AI search agents debug their own prompts by comparing successful and failed attempts. This could make AI search tools like chatbots and assistants more accurate and reliable over time.
Scientists found that editing one part of an AI's instructions can unintentionally change other parts. This happens because AI models share a common context window, causing unexpected behavior. This is a problem for developers who use AI to build complex systems.
Researchers found that placing questions in the right spot within AI prompts can significantly improve answers. This discovery is especially important for newer AI models that process information differently than older ones.
Researchers introduce COMPASS, a system that automates prompt engineering for generating human-understandable explanations of AI task planning. The tool addresses the critical need for reliable, stakeholder-specific explanations in complex software systems.
OpenAI has released a guide on using ChatGPT to create and refine images through clear prompts. The guide highlights the efficiency and creative potential of AI-driven visual content generation.