AdaptPrompt: A Framework for Adaptive and Efficient Prompt Engineering in Large Language Models

Prompt engineering is a very important process for optimizing large language models (LLMs) in applications like natural language processing (NLP), knowledge extraction, and content generation. Many existing frameworks, however, lack the flexibility and real-time feedback systems necessary to prevent model hallucinations and conflicting outputs. This paper introduces AdaptPrompt, an adaptive prompt engineering system developed to transcend these restrictions by embracing dynamic constraints, real-time feedback loop, and adaptive prompt synthesis. The system dynamically alters prompts on on user interaction, which greatly enhances task relevance and performance in real-world applications such as conversational chatbots, specialized content development and appropriate data mining. By providing pre-built templates for novices and high customization for experts, AdaptPrompt improves usability and reduces model hallucinations and output quality improvement. Comparison of AdaptPrompt to various other methods was carried out, with a rating of 4.7/5 for improvement in output quality, a 4.5/5 user experience rating, and a 30% reduction in the time needed to generate high-quality outputs. AdaptPrompt enables better prompt crafting, thus rendering it a viable tool for building strong and user-focused text-based AI applications.