The field of large language models (LLMs) is rapidly advancing, with a growing focus on developing structured approaches to prompt engineering and integrating LLMs into research workflows. Recent developments highlight the potential of LLMs to enhance civic engagement, improve government transparency, and support specialized tasks such as document analysis and code synthesis. Notably, researchers are working to address challenges related to model variability, prompt design, evaluation protocols, and transparent reporting practices. A key direction for the field is the development of systematic methodologies for using LLMs, such as the TaMPER framework, which provides a structured approach to leveraging LLMs effectively. Additionally, there is a growing need for responsible innovation, particularly in the financial sector, where LLMs are being adopted to enhance credit assessments and deliver personalized client advisory services. Noteworthy papers include:

• GRASP: Municipal Budget AI Chatbots for Enhancing Civic Engagement, which proposes a custom AI chatbot framework that provides more truthful and grounded responses to user budget queries than traditional information retrieval systems.
• TaMPERing with Large Language Models: A Field Guide for using Generative AI in Public Administration Research, which introduces a structured methodology for using LLMs in public administration research.
• From Prompts to Templates: A Systematic Prompt Template Analysis for Real-world LLMapps, which presents a comprehensive analysis of prompt templates in practical LLMapps.
• Responsible Innovation: A Strategic Framework for Financial LLM Integration, which introduces a structured framework for responsibly integrating LLMs into financial institutions.