The AI Workshop (Milo Foster) - Amazon USA Store: https://www.amazon.com/dp/B0F5P3RR81?tag=9natree-20 - Amazon Worldwide Store: https://global.buys.trade/The-AI-Workshop-Milo-Foster.html - Apple Books: https://books.apple.com/us/audiobook/the-ai-blueprint-for-beginners-your-beginners/id1835978893?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree - eBay: https://www.ebay.com/sch/i.html?_nkw=The+AI+Workshop+Milo+Foster+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1 - Read more: https://mybook.top/read/B0F5P3RR81/ #ArtificialIntelligence #NocodeAI #Promptengineering #AIstrategy #Businessautomation #GenerativeAI #EthicsinAI #Productivity #TheAIWorkshop These are takeaways from this book. Firstly, Demystifying AI Fundamentals for Absolute Beginners, The book opens by making core ideas simple and practical. Artificial intelligence is framed as a set of pattern systems that transform inputs into useful outputs you can verify. You learn how machine learning differs from traditional rule based software, and why modern models predict the next token rather than search a fixed script. Concepts like training versus inference, parameters, context windows, and embeddings are explained with plain language examples that you can test in minutes. The text clarifies the strengths and limits of large language models, vision models, and multimodal systems, so you understand when to trust them and when to add human checks. It also addresses common pitfalls such as hallucinations, brittle prompts, and data leakage, and shows how to reduce risk with structured tasks and validation. Instead of abstract theory, the author gives you lightweight mental models that stick, a glossary for quick reference, and small exercises that build confidence. By the end of this section, the jargon fog lifts and you can navigate AI conversations, evaluate claims, and select the right tool for the job without feeling overwhelmed. Secondly, Prompt Craft, System Design, and Repeatable Workflows, This section turns raw prompting into a reliable process. You start by clarifying objectives, audiences, and guardrails, then design a system prompt that sets role, tone, scope, and success criteria. From there, you learn a simple cycle to improve results over time: clarify the task, anchor the context, refine the prompt with constraints and examples, and evaluate outputs against checklists. The book teaches pattern prompts for summarization, transformation, ideation, decision support, and planning, plus how to chain steps so each output feeds the next. You practice giving models structure with outlines, tables, and rubrics, using evaluation prompts to score quality and consistency. The author shows how to build libraries of reusable prompts, templates, and snippets so your best work becomes a sharable asset. You also learn how to mix tools, for example pairing a model with a search step, a spreadsheet, or a form to collect inputs. The result is a repeatable workflow you can document, measure, and hand to a colleague, turning one off experiments into dependable processes that save time every week. Thirdly, Data Literacy, Measurement, and Responsible Use, Effective AI practice requires good data habits and clear evaluation. This part teaches you how to source and prepare data with consent, document provenance, and protect sensitive information with redaction, access controls, and least privilege principles. You learn to set acceptance criteria that define what good looks like, and to measure results with both quantitative metrics and human review. Coverage includes precision and recall in plain language, prompt reliability checks, and lightweight A B testing to compare approaches. The book explains bias, fairness, and representativeness, offering practical st...