Bonuses

Appendix A

A Simple AI Cheat Sheet

You’ve already learned far more than what’s on this page. This is the version you can tape up next to your computer.

Start clear. AI responds to your prompt, and the clearer your question the more useful the answer. “Help me plan dinner” produces something generic. “I have meatballs, noodles, and beans and I need dinner for six in forty-five minutes” produces something you can use. You’ve seen this pattern throughout the book: specificity is the difference between a vague suggestion and a real answer.

Ask for a revision. If the first response isn’t quite right, just say so. Make this shorter. Explain this more simply. Give me three alternatives. The conversation doesn’t have to end with the first answer, and most of the best results come from the second or third exchange.

Give it context. A short explanation of what you’re trying to accomplish, who it’s for, what you already know, what you need, often changes the quality of the response entirely. AI doesn’t know your family, your budget, or your constraints unless you tell it. The more it understands about what you’re working with, the less you have to sort through afterward.

Let it handle the sorting. AI organizes information quickly, summarizing reviews for three summer camp companies, turning a rough chore rotation into a printable chart, or pulling together a capsule wardrobe checklist by season and climate. The value isn’t that it knows more than you do. The value is that it does the tedious assembly work, so you can spend your energy on the decisions that give you more time for your family.

Apply your own judgment. AI generates responses based on patterns, not certainty. When something matters, like health, finances, safety, or a major decision, verify what it tells you before you act on it. This is especially true for anything involving your children. The tool is useful precisely because you remain the one in charge of what to do with its output.

Use it to think more strategically, not to stop thinking. This is the idea that holds all the others together. The most useful thing AI can do is help you work through something you already care about: bring it a real problem, ask it a real question, and let it help you see what you might not have noticed on your own. The thinking still belongs to you, and it always will.

When in doubt, start with a single honest question about something that matters to your family today, and build from there. 

Appendix B

AI Terms (For When You’re Curious)

You do not need to understand these terms to use AI well. This appendix exists so that nothing about AI feels mysterious or intimidating. If you encounter a word in an article, a conversation, or a tool and wonder what it means, you can look it up here and keep moving. Skim it, consult it occasionally, or skip it entirely. Ask AI if you want more information.

How it works behind the scenes

Artificial Intelligence (AI) — Computer systems that perform tasks that normally require human thinking, such as summarizing information, organizing ideas, explaining complex topics, or generating writing. AI is not a replacement for human thinking but a tool that helps you organize and explore information more quickly.

Generative AI — AI systems designed to create new content rather than simply analyze existing data. Generative AI can write text, generate images, suggest ideas, summarize research, or draft plans. The key word is generate: the system produces something new based on patterns it has learned.

Model — The trained system that produces AI responses. Different models are built for different tasks, the way different tools in a workshop serve different purposes, some better at writing, others at coding, reasoning, or research.

Large Language Model (LLM) — A type of AI trained to understand and generate human language by learning patterns from extremely large collections of text. ChatGPT, Claude, Copilot, and Gemini are examples of large language models.

Training — The process by which an AI system learns patterns from large amounts of information before it is released for public use. A useful analogy: training is studying for a test. The system absorbs patterns during training so it can apply them when responding to questions.

Inference — The stage when the AI generates a response to your prompt. To continue the analogy, training is studying and inference is taking the test.

Context Window — The amount of information an AI can hold in mind during a conversation, the system’s short-term memory. If a conversation becomes very long, earlier details may drop out of the context window, which is why restating key information can sometimes help.

Token — A small unit of text used by AI systems when reading or generating language. Many AI services measure usage by tokens rather than words. For everyday use this detail rarely matters.

How you interact with AI

Prompt — The instruction or question you give the AI. Clear, specific prompts produce more useful answers than vague ones, and adding context about what you’re trying to accomplish almost always improves the response.

Prompt Engineering — The practice of writing clearer, more effective prompts. You have been doing this throughout the book, structuring your questions, providing context, refining based on what comes back.

Chat History / Conversation Thread — Most AI tools remember what has been said earlier in the conversation, so the system can maintain context and build on previous answers.

Terms you may encounter

AI Agent — An AI system that can take multiple steps to complete a task on its own, rather than simply answering a single question. An agent might research several options, compare them, and present a recommendation, handling a sequence of actions rather than a single exchange.

Retrieval-Augmented Generation (RAG) — An approach where AI looks up information from external sources before generating an answer, similar to a person consulting reference material before responding to a question.

Knowledge Base — A structured collection of documents or information that an AI system can reference. Organizations often build knowledge bases so AI can answer questions about their products, policies, or research.

Semantic Search — A search method that focuses on meaning rather than exact keywords. Instead of matching precise phrases, the system attempts to understand the concept behind the question.

Tools you may encounter

Chatbot — An AI interface designed for conversation. Many customer service systems and AI assistants use chatbot-style interfaces.

Copilot — An AI assistant integrated directly into another application, such as a writing tool, spreadsheet, or design platform.

Speech-to-Text — Technology that converts spoken words into written text. Many people use it to capture ideas quickly or dictate notes.

Image Generation — AI tools that create images from written descriptions.

Ethics, safety, and good judgment

Bias — AI systems learn patterns from existing data, and those patterns can reflect historical biases. Human judgment is always necessary when interpreting AI responses.

Hallucination — When AI generates information that sounds convincing but is incorrect. This happens because AI predicts likely language patterns rather than verifying facts. Always check important information before acting on it.

Guardrails — Safety boundaries built into AI systems to prevent harmful or inappropriate outputs.

Human in the Loop — A principle that keeps people responsible for decisions rather than allowing AI to act independently. AI can suggest and organize; people remain accountable for what happens next.

About the Author

I am the mother of four teenagers and young adults, a stepmother to a son raised in New York City, and an unofficial extra parent to six former foreign exchange students who became part of our extended family. The ideas in this book were not developed in theory or under perfect conditions. They have been tested through school schedules, teenage transitions, budget decisions, health challenges, and the ordinary work of building strong relationships inside a busy home.

For many years I was a full-time stay-at-home mother, and during that time I stayed engaged in learning, systems thinking, and ongoing research while freelancing as a web designer and marketing director. My husband was serving as an executive at a digital learning company, and conversations about education technology and emerging tools were a regular part of our home life. I did not step away from growth during those years. I redirected it.

Much of what later shaped my professional work began as experiments inside our home, building practical systems that helped a busy family function well, encouraging responsibility in children, and thinking about how new technologies might support the work of raising capable adults rather than replacing it.

Today I work in product marketing for a major smart home technology company, helping bring advanced networking and AI-enabled infrastructure products to market. Before that I worked in technical education, contributing to the launch of IT, cybersecurity, and networking courses and internationally recognized certifications, and helping roll out digital and AI literacy programs through universities and global training platforms like Amazon and Coursera. That work confirmed something I believe: AI literacy matters, but only when it’s grounded in clear thinking and real-world application.

Earlier in my family life I was honored as Oregon’s Young Mother of the Year and represented the state at the national American Mothers convention. I later served as a delegate at the United Nations World Family Summit and the World Congress of Families in Warsaw, Poland, and contributed to NGO work with the World Bank promoting digital literacy in developing countries. Those experiences deepened a conviction that had already taken root in my own life: family life is not separate from leadership. It is leadership.

I also support SheTech, an organization dedicated to helping girls build confidence and pursue opportunities in technology. My husband founded a media publication covering emerging AI and technology startups across Utah, so questions about innovation and entrepreneurship come up often at our dinner table, right alongside questions about character, responsibility, commitment, and joy.

I write and speak about family life, practical systems thinking, and responsible AI adoption. I live near Salt Lake City, Utah, with my family and our cat.