Contextual and Task-Specific Prompting

Contextual and Task-Specific Prompting

Arjun Bali (The University of Texas at Dallas, USA), Siddharth Kashiramka (Emory University, USA), Anshuman Guha (The Johns Hopkins University, USA), and Prashant Gupta (University of California, Davis, USA)
Copyright: © 2026 |Pages: 30
DOI: 10.4018/979-8-3373-0250-8.ch002
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Abstract

The chapter provides a comprehensive approach to learning contextual and task-specific prompting, two essential components for enhancing interactions with AI language models. Initially, the chapter examines the significance of contextual prompting. The chapter then explores task-specific prompting, illustrating how prompts can be tailored to meet the distinct requirements of diverse jobs, ranging from factual questions to creative content development. The chapter then address the challenges of prompts in a multi-lingual and cross-cultural setting, and how best tackle these challenges. At the end, we look at how we see the future of contextual prompting, focusing on adaptive prompts, personalized prompt design, and ethical considerations. On completing this chapter, readers will have a thorough understanding of how to provide accurate and pertinent prompts that enhance AI capabilities, while putting these methods within the broader context of innovation in AI applications
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