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Book Cover for: Prompt Engineering: A Structured Approach, Roshan Fernandes

Prompt Engineering: A Structured Approach

Roshan Fernandes

As artificial intelligence systems grow more capable, accessible, and deeply embedded in everyday work, the ability to communicate effectively with them has become a defining skill for professionals across disciplines. Prompt engineering, once an informal practice of trial-and-error, has evolved into a structured craft grounded in design principles, evaluation methods, and an understanding of how modern language models interpret and generate information. This book offers a comprehensive foundation for that craft, guiding readers from fundamental concepts such as prompt structure and model behaviour, through advanced patterns like chain-of-thought reasoning, multimodal prompting, and human-in-the-loop collaboration. Drawing on both conceptual frameworks and practical examples, it equips practitioners to design prompts that are clear, robust, safe, and aligned with real-world objectives.

Yet effective prompt engineering is not only about writing good instructions, it is about building repeatable systems. As organisations adopt AI tools at scale, teams must be able to evaluate prompt performance, guard against failure modes, operationalise workflows, and ensure responsible, ethical use. The chapters that follow offer a structured roadmap for this emerging discipline, from diagnosing prompt failures to constructing prompt libraries, implementing guardrails, and preparing for the future of agentic AI. Whether you are a developer, analyst, educator, designer, or leader, this book provides the tools and insights needed to transform prompting from an ad-hoc skill into a reliable component of modern AI practice.

Book Details

  • Publisher: Independently Published
  • Publish Date: Dec 11st, 2025
  • Pages: 310
  • Language: English
  • Edition: undefined - undefined
  • Dimensions: 10.00in - 8.00in - 0.65in - 1.36lb
  • EAN: 9798278286301
  • Categories: Artificial Intelligence - Generative AI