The complete practitioner's guide to AI transformation for B2B SaaS companies. Covers strategy, technical architecture, RAG systems, 90-day sprint planning, team adaptation, and long-term AI-native operations. Built around Nexus Technologies, a realistic case study followed across 18 months of transformation. Written for CTOs, VPs of Engineering, and technical founders who need to make real decisions, not theory.
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Maker
π
I wrote this book after working inside and alongside B2B SaaS companies navigating AI transformation. Most teams I worked with had the same problem: high ambition, genuine uncertainty about where to start, and no structured playbook to follow. This is that playbook β from the first use case prioritization decision through 18 months of transformation, built around a realistic fictional company (Nexus Technologies) so the frameworks stay concrete and applicable. Happy to answer any questions about the content or the approach.
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How deep does the technical architecture section actually go on RAG systems, like are we talking high-level patterns or actual implementation details with code examples for evaluating retrieval quality?
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Maker
@tahirqj7zΒ The RAG chapter goes deeper than high-level patterns but stops short of code-level implementation. You'll find detailed coverage of chunking strategies, metadata design, retrieval evaluation methodology (precision/recall tradeoffs, chunk relevance scoring), and the specific failure modes that production RAG systems hit with diagnostic frameworks for each. There are no code examples; the book is written for technical leaders making architecture and evaluation decisions, not for engineers implementing from scratch. If you're looking for a hands-on coding guide, this isn't it but if you're trying to understand what makes a RAG system production-ready and how to evaluate whether yours is working, it covers that in depth.
How deep does the technical architecture section actually go on RAG systems, like are we talking high-level patterns or actual implementation details with code examples for evaluating retrieval quality?
@tahirqj7zΒ The RAG chapter goes deeper than high-level patterns but stops short of code-level implementation. You'll find detailed coverage of chunking strategies, metadata design, retrieval evaluation methodology (precision/recall tradeoffs, chunk relevance scoring), and the specific failure modes that production RAG systems hit with diagnostic frameworks for each. There are no code examples; the book is written for technical leaders making architecture and evaluation decisions, not for engineers implementing from scratch. If you're looking for a hands-on coding guide, this isn't it but if you're trying to understand what makes a RAG system production-ready and how to evaluate whether yours is working, it covers that in depth.