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The Claude Playbook Series two-volume set — enterprise Claude AI guide by Bernard W. Piccione
Buy Volume 1 in hardcover on Amazon
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Hardcover
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$49.95
Publisher
Independently published (Amazon KDP)
ISBN
9798188377960
AI governance playbook for enterprise IT

The Claude Playbook Series

The two-volume enterprise guide to Claude AI — from first prompt to governed, production-grade deployment.

Most enterprise AI programs do not fail on model quality. They fail because nobody defined who may use the tool, on which data, with what review step, and how the result gets audited six months later. The Claude Playbook Series closes that gap in two volumes: the first takes a working professional from first prompt to competent daily operator, the second takes an IT organization from pilot to governed deployment — with the controls, prompt libraries, and diagrams a CIO can hand to a team on Monday morning.

From the back cover

The Complete Guide to Claude AI — From First Prompt to Power User. 20 diagrams and 175+ ready-to-use prompts.

  • Volume 1 (The Claude Playbook): beginner fundamentals including prompt engineering and data analysis.
  • Volume 2 (The Hidden Playbook): advanced features, power prompts, and apps that think.
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Written for
  • CIOs and IT directors who have been asked for an AI plan by their CEO or board and need something more concrete than a vendor deck.
  • Enterprise architects and platform owners designing the guardrails around a general-purpose assistant.
  • Risk, privacy, and compliance leads who need to write an acceptable-use standard that engineers will actually follow.
  • Analysts, PMs, and knowledge workers who want to move past copy-paste prompting into repeatable workflows.
Not written for
  • Readers looking for model-training or fine-tuning theory — this is an adoption and governance playbook, not an ML textbook.
  • Teams wanting a single vendor-specific certification path.
Framework

What the two volumes cover

  1. 01

    Foundations: how the assistant actually behaves

    Context windows, memory boundaries, refusal behaviour, and where hallucination risk concentrates — explained for decision-makers, not researchers.

  2. 02

    Prompt engineering as an operating discipline

    Role, constraint, format, and evidence patterns; how to turn a good one-off prompt into a reusable team asset.

  3. 03

    Document, research, and data-analysis workflows

    Long-document review, comparative research, spreadsheet reasoning, and the review step each one requires before output is trusted.

  4. 04

    No-code and low-code build patterns

    Where an assistant-built internal tool is appropriate, and where it quietly becomes unsupported shadow IT.

  5. 05

    Governance, acceptable use, and data classification

    Mapping data classes to permitted AI use, retention posture, human-in-the-loop thresholds, and the audit trail your auditors will ask for.

  6. 06

    Rollout, enablement, and measurement

    Cohort-based rollout, champion networks, and the handful of metrics that show whether adoption is real or performative.

  7. 07

    175+ ready-to-use prompts and 20 diagrams

    Organised by function — IT operations, security, finance, HR, project delivery — so a team can start from a tested baseline.

From the book

An assistant with no data-classification policy behind it is not a productivity tool. It is an unlogged export channel.

The Claude Playbook Series

The right first question is never "which model is best?" It is "which decisions are we willing to let a model touch, and who signs off?"

The Claude Playbook Series

Prompting stops being a trick and starts being an asset the moment you version it, review it, and assign it an owner.

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