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AI Prompt Engineering: Foundations of Communication with LLMs – Building Generative AI and Agentic AI Prompt Systems Across Development, Testing, and Deployment
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Displaying 1 - 3 of 3 reviews
September 16, 2026
Some sentences had missing words. Some of the advice was blatantly obvious such as how the subject is important for image prompts and how specific terms produce better results than vague wording. I did like the author's writings on treating AI systems as software such as through version control, the use of the single responsibility pattern, explicit input and output schemas and error handling, and A/B testing. He was right that prompting can be a disciplined activity instead of guesswork.
December 23, 2025
Beyond surface-level prompting!
AI Prompt Engineering: Foundations of Communication with LLMs breaks prompting down into three essential layers: intent, information, and control.
Nelson Ming shows how each layer shapes model behaviour in predictable ways, transforming prompting from trial-and-error experimentation into a reproducible design process.
By treating roles, goals, tasks, context, constraints, style, and output format like modular building blocks, Ming shows you how to reuse and refine prompts across projects. The practical and well-chosen examples demonstrate how small changes in role definition or output structure can dramatically improve results.
Ming emphasizes clarity, explainability, and behavioural control, including constraints and reasoning chains when consistency matters. Prompts can integrate live or proprietary data without retraining models using Retrieval Augmented Generation (RAG).
Ming also positions prompt engineering as a shared interface between technical and creative disciplines. He reinforces the idea that prompt engineering is a new form of communication shaping how humans guide intelligent systems.
If you’re looking for a solid foundation that combines theory, structure, and practical insight, this serious, structured guide is an excellent resource.
AI Prompt Engineering: Foundations of Communication with LLMs breaks prompting down into three essential layers: intent, information, and control.
Nelson Ming shows how each layer shapes model behaviour in predictable ways, transforming prompting from trial-and-error experimentation into a reproducible design process.
By treating roles, goals, tasks, context, constraints, style, and output format like modular building blocks, Ming shows you how to reuse and refine prompts across projects. The practical and well-chosen examples demonstrate how small changes in role definition or output structure can dramatically improve results.
Ming emphasizes clarity, explainability, and behavioural control, including constraints and reasoning chains when consistency matters. Prompts can integrate live or proprietary data without retraining models using Retrieval Augmented Generation (RAG).
Ming also positions prompt engineering as a shared interface between technical and creative disciplines. He reinforces the idea that prompt engineering is a new form of communication shaping how humans guide intelligent systems.
If you’re looking for a solid foundation that combines theory, structure, and practical insight, this serious, structured guide is an excellent resource.
May 5, 2026
Structured detailed in depth explanation of how exactly to prompt AI
Based on a deep knowledge of how LLMS are trained and fine tuned "AI Prompt Engineering" provides you a detailed theoretical and practical explanation about exactly how to prompt llms most effectively. It is the rare book that manages to successfully combine theory and practice. As well as being detailed it is thorough. T
This book provides you plenty of example prompts that are very useful to illustrate the ideas. It is well structured because it is organized by the logic of how an llm is formed. For example, the book explains exactly what "alignment" is and how to align your prompts to better generate the quality of answer you desire. One-shot, multi-shot, chain-of-thoughts, role-playing, all forms of prompting are in here and they are explain in great detail with plenty of examples including many prompts to illustrate the theoretical principles in practice. The book is not geared to a particular language model, nor need it be: it applies equally well to any of the main llms since they are all formed up on the same underlying neural-network architecture, albeit with different training data, constraints, and fine tuning instructions.
Great book especially if you are serious about effective prompting and want to get the most mileage possible out of your AI workflow.
Five Stars.
Based on a deep knowledge of how LLMS are trained and fine tuned "AI Prompt Engineering" provides you a detailed theoretical and practical explanation about exactly how to prompt llms most effectively. It is the rare book that manages to successfully combine theory and practice. As well as being detailed it is thorough. T
This book provides you plenty of example prompts that are very useful to illustrate the ideas. It is well structured because it is organized by the logic of how an llm is formed. For example, the book explains exactly what "alignment" is and how to align your prompts to better generate the quality of answer you desire. One-shot, multi-shot, chain-of-thoughts, role-playing, all forms of prompting are in here and they are explain in great detail with plenty of examples including many prompts to illustrate the theoretical principles in practice. The book is not geared to a particular language model, nor need it be: it applies equally well to any of the main llms since they are all formed up on the same underlying neural-network architecture, albeit with different training data, constraints, and fine tuning instructions.
Great book especially if you are serious about effective prompting and want to get the most mileage possible out of your AI workflow.
Five Stars.
Displaying 1 - 3 of 3 reviews