From vegetable market chains in Ethiopia From prompt engineering to context engineering: a comparative analysis of AI interaction paradigms for large language models

Abstract

The rapid evolution of Large Language Models (LLMs) has increased the need for better ways to support human AI interaction. Prompt engineering is the most common method for guiding model behavior through carefully written instructions. It works well for many tasks, but it often falls short in complex and production level applications. This paper presents a comparative analysis of prompt engineering and context engineering. Context engineering moves beyond tuning a single prompt and focuses on designing the whole system around the model. We use illustrative examples and a structured comparison to explain the differences and the trade offs. The results show that prompt engineering fits simple one shot queries that need little state and few extra resources. Context engineering supports scalable and dependable agents that can use documents, memory, tools, and prior interactions. Our analysis shows that the best choice depends on application complexity, reliability needs, and scalability goals. We also synthesize design guidelines from prior literature and from Anthropic's context engineering framework. This work adds to research on AI system design and discusses implications for AI literacy, software engineering practice, and human AI collaboration.