The field of large language models (LLMs) is rapidly evolving, with a focus on improving performance in complex tasks such as classification, summarization, and reasoning. Recent studies have highlighted the importance of prompt engineering and optimization in achieving accurate and robust results. The development of frameworks and tools, such as PromptBridge and promptolution, has enabled the transfer of prompts across models and the optimization of prompt performance. Additionally, research has shown that incorporating label definitions in prompts and using techniques such as cross-model prompt transfer can improve classification accuracy. The use of LLMs in domains such as finance and psychology has also been explored, with applications in risk-of-bias assessment, prediction markets, and construct identification. Notable papers include: A Comparison of Human and ChatGPT Classification Performance on Complex Social Media Data, which highlights the limitations of ChatGPT in classifying nuanced language. Mitigating Hallucinations in Zero-Shot Scientific Summarisation: A Pilot Study, which demonstrates the effectiveness of prompt engineering in reducing hallucinations in scientific summarization. PromptBridge: Cross-Model Prompt Transfer for Large Language Models, which introduces a framework for cross-model prompt transfer. Automated Risk-of-Bias Assessment of Randomized Controlled Trials: A First Look at a GEPA-trained Programmatic Prompting Framework, which presents a programmable pipeline for risk-of-bias assessment. DETAIL Matters: Measuring the Impact of Prompt Specificity on Reasoning in Large Language Models, which highlights the importance of prompt specificity in improving reasoning accuracy. Detecting AI Hallucinations in Finance: An Information-Theoretic Method Cuts Hallucination Rate by 92%, which proposes a framework for detecting hallucinations in financial question answering.