Thinking in Prompts. Seven Principles for Clear, Reliable AI Results

You already use AI. But the feeling stays: there is more here. Sometimes ChatGPT lands exactly on your point. Sometimes the model speaks past you. Sometimes the output sounds generic and interchangeable. The tool is powerful, but the results are too often a matter of chance. That is not a technology problem. It is a method problem. Thinking in Prompts gives you seven timeless principles that turn random hits into reliable results.
You learn how large language models like GPT, Claude and Gemini actually process information, and how to use that knowledge to direct the model instead of hoping for the best. From the single question to multi-step workflows, from the basic prompt to the mega prompt, from chain of thought and few-shot mastery to retrieval augmented generation and autonomous AI agents. No prior knowledge required.
No jargon. A practical method, tested with more than a thousand professionals and senior leaders. The seven principles: Clarity, Specificity, Context, Structure, Role, Examples and Iteration. They work model-independent, whether you use ChatGPT, Claude, Gemini, Mistral, Llama or whatever ships next. Inside: modular reusable system prompts, reasoning techniques, multi-persona workflows, multimodal prompting, prompt chaining and agents, domain frameworks for law, finance, consulting and medicine, retrieval augmented generation (RAG), plus governance and the EU AI Act.
More than one hundred ready-to-use templates included. Volume 2 of the standard work on prompt engineering. If you read Volume 1, The Prompt Engineer's Handbook (Amazon Top 50 in AI worldwide), you step in deeper from page one. If you start here, you will find your bearings fast.