# Amazon description — Prompt Engineering Mastery: The Definitive Guide to LLMs, Advanced RAG, and Agentic Workflows (2026 Edition)
# ASIN: B0GS77LKB2
# Series: The 1'st AI Engineering Series

If you already know how to write a prompt — this is the book that shows you how to build systems around them.

Prompt Engineering Mastery is the advanced practitioner's guide for teams shipping LLM systems at scale: complex RAG pipelines, agentic workflows, security hardening, and evaluation infrastructure. It is the direct systems-level continuation of the prompting craft.

THIS BOOK IS FOR YOU IF:

• You're building RAG pipelines and need faithfulness consistently above 90%
• You're designing agentic workflows where prompt failures have cascading consequences
• You need to defend against prompt injection in a production application
• You want to evaluate prompts systematically with eval sets, not by feel
• You need a current reference for GPT-4o, Claude 3.x, and Gemini 1.5 — updated for 2026

New to prompt engineering? Start with The Art of Prompt Engineering or Prompt Engineering: From Basics to Advanced.

THREE PRODUCTION CASE STUDIES — FULL BEFORE/AFTER IN PART IV:

• Contract extraction: Manual review 40% → 16%. Three prompt failures identified and fixed. Same model.
• Document QA: RAG faithfulness 70% → 94%. One grounding instruction change. Full prompt included.
• Support chatbot: Escalation rate reduced 67% through refusal boundary redesign.

2026 EDITION — WHAT'S UPDATED:

• Model-specific patterns for GPT-4o, Claude 3.x, Gemini 1.5
• Security chapter: 6 new injection attack patterns from production environments
• Agentic prompt design: dedicated coverage — fundamentally different from chat prompting
• RAG chapter: updated with current production case studies

17 CHAPTERS + APPENDIX B: Battle-Tested Prompt Templates — production-ready, tested against real systems, copy and adapt immediately.

The patterns that reduced manual review by 60%, improved RAG faithfulness to 94%, and cut support escalations by 67% are documented in full — including the prompts that failed and the specific changes that fixed them.