Prompt Engineering Mastery: Get More from Every AI Model

The same question, asked two ways, gets two very different answers. Prompt engineering is the discipline of understanding that variance and exploiting it.

When conversational language models arrived, the prevailing assumption was that talking to them would be easy — you just ask. It turned out that how you ask determines almost everything: whether the model reasons or guesses, whether it stays in format or drifts, whether it costs cents or dollars to run at scale.

Prompt Engineering Mastery brings together the full spectrum of that discipline as it stands in mid-2026, from the mental model of how models actually work through to running prompts in production.

What you'll work through:

• Foundations — how language models think: tokens, attention, sampling, training and hallucination, and why prompts succeed or fail
• The core techniques — zero-shot prompting and the anatomy of a single instruction; few-shot prompting and how to choose, order and format examples; chain-of-thought, self-consistency and tree of thoughts
• System prompts — the highest-leverage control surface, including persona design and prompt-injection protection
• Patterns — reusable templates and chains, a named pattern catalogue, and reasoning patterns like ReAct, reflection and step-back
• Agents and multimodality — function calling, the agent loop, memory and orchestration; prompting across images, documents, audio and video
• Across models — profiles of the major model families and how to write portable prompts
• Optimisation and evaluation — evaluation datasets, metrics, A/B testing, automated optimisation, and token efficiency and compression
• Production — RAG, caching, latency, cost, security and monitoring at scale
• Ethics and responsibility — bias, hallucination, jailbreaks, disclosure and regulation
• Applied — domain-specific prompting for healthcare, legal, finance, education, software and creative work; complete AI application architecture; industry case studies; and team, process and enterprise governance

Fourteen appendices close the book: quick references, model comparison tables, evaluation rubrics, checklists, a complete prompt library, a glossary, interview questions, and cross-cultural guidance.

Written for software engineers building AI-powered features, product managers trying to understand why an AI feature behaves the way it does, and anyone who has felt the gap between what a model can do and what they can get it to do. The only prerequisites are the first three chapters.