The field of prompt engineering for Large Language Models (LLMs) is rapidly evolving, with a focus on developing innovative approaches to improve the efficiency, flexibility, and reliability of LLM interactions. Recent developments suggest a shift towards more systematic and explicit frameworks for prompt design, enabling better control and customization of LLM outputs. Notably, researchers are exploring novel representations of prompts, such as the Prompt Declaration Language (PDL), and minimalistic design frameworks like the 5C Prompt Contract. Additionally, advances in state-inference-based prompting and retrieval-augmented generation are being applied to real-world applications, including natural language trading and requirements engineering in the space industry. These innovations have the potential to significantly enhance the performance and trustworthiness of LLMs in various domains. Noteworthy papers include:

• Representing Prompting Patterns with PDL, which demonstrates a novel approach to prompt representation and achieves up to 4x performance improvement.
• 5C Prompt Contracts, which proposes a minimalist design framework that consistently achieves superior input token efficiency while maintaining rich and consistent outputs.
• State-Inference-Based Prompting for Natural Language Trading with Game NPCs, which enables reliable trading through autonomous dialogue state inference and context-specific rule adherence.
• From Domain Documents to Requirements, which explores the potential of Retrieval-Augmented Generation models to support and automate requirements generation in the space domain.
• PyVision, which presents an interactive framework that enables MLLMs to autonomously generate and refine Python-based tools tailored to the task at hand.