This chapter develops the concept of precision prompting as a disciplined approach to controlling language model behavior through structured, context-aware instructions. It distinguishes precision from verbosity, emphasizing informational density, constraint definition, and alignment with task objectives. Building on prior chapters, it introduces techniques such as precision scaffolding and prompt layering to guide complex outputs through staged, multidimensional control. The discussion highlights how task decomposition, audience specification, and format constraints improve reliability, interpretability, and professional usability. It further examines methods for diagnosing weak prompts and systematically rewriting them using a structured design framework. Prompt templates are presented as reusable, scalable tools that embed consistency, ethical considerations, and domain-specific standards into AI interactions. The chapter also explores real-world prompting patterns across domains such as policy, research, healthcare, and communication, demonstrating how precision supports clarity and reproducibility. Emphasis is placed on auditability, transparency, and the role of prompting in high-stakes environments. Precision prompting is framed as both a linguistic and ethical responsibility in human–AI collaboration. Ultimately, the chapter positions granular prompt design as essential for producing accurate, consistent, and professionally aligned AI-generated outputs.