Prompt Engineering vs Context Engineering: A Strategic Framework for Insurance AI Applications

The rapid evolution of artificial intelligence in the insurance sector has led to transformative changes in underwriting, claims processing, risk management, customer service, and compliance. In particular, the engineering of input prompts and contextual information for large language models (LLMs) has emerged as a critical factor in achieving reliable, explainable, and efficient AI systems. This paper compares two prominent approaches: prompt engineering, which leverages task instructions and demonstration examples (e.g., zero-shot and few-shot techniques), and context engineering, which systematically structures diverse sources of information into dynamic and modular inputs for LLMs. Drawing upon extensive research on context engineering methodologies alongside practical insights from insurance AI applications, we propose a strategic decision framework to guide stakeholders in selecting the most suitable engineering method for specific use cases in underwriting, fraud detection, claims automation, and regulatory compliance. The framework is substantiated with empirical metrics, visualization of decision flows, and detailed case studies illustrating performance, transparency, and explainability improvements. Our findings underscore that while prompt engineering offers simplicity and ease-of-deployment for less complex tasks, context engineering provides enhanced performance, stability, and explainability for high-stakes insurance applications.