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Practical AI with Google Apps Script Building Trusted AI Tools for Google Workspace

A practical field guide to building trustworthy AI tools inside Google Workspace with Apps Script, where AI advises but code decides and humans stay in control.

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What it’s about

Practical AI with Google Apps Script cuts through AI hype to teach developers, educators, and teams how to build real, deployable AI tools inside the Google Workspace environment they already use. Instead of chasing model internals or flashy demos, the book establishes a durable philosophy—AI informs, code decides, humans approve—and shows how to apply it across Docs, Sheets, Gmail, Forms, and Drive. Readers learn how AI actually works (tokens, context windows, embeddings, hallucinations), safe API-calling patterns, prompt engineering, structured outputs and guardrails, RAG-Lite over Drive, sidebar and add-on design, tutors and knowledge bots, responsible AI and privacy, performance and scaling, long-term maintenance, and complete capstone architectures. The result is a mental model and reusable toolkit for shipping AI that survives IT reviews, institutional policy, and daily real-world use.

The through-line

Who it’s for
A developer, educator, or team member who uses Google Workspace and wants to build AI tools that actually work and are trusted inside real organizations.
The problem
AI demos look impressive but break, hallucinate, and fail under real usage, policies, and reviews. They feel they can't trust AI output and worry about shipping something that embarrasses them or harms users.
The plan
  1. Learn how AI actually works and where it fails.
  2. Call AI APIs safely from Apps Script with secure keys, retries, and caching.
  3. Engineer reliable prompts and enforce structured outputs with validation and repair.
  4. Integrate AI into Docs, Sheets, Gmail, Forms, and Drive using human-in-the-loop patterns.
  5. Apply responsible AI, security, performance, and maintenance practices.
The payoff
You ship AI tools people trust and use every day. · Your tools survive IT review, institutional policy, and real-world load. · You think like a production AI architect and system designer, not a prompt experimenter.

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