Consulting

Reliable Generative AI bridges business use and technical architecture. It teaches the foundations of prompt design, RAG, agentic workflows, tool use, structured outputs, safety patterns, and evaluation without assuming the reader is a software engineer. The focus is practical: understanding how AI workflows fail, how to design around those failures, and how to build systems that professionals can trust.

One book rarely changes your life. A system of books can.Learn how to build a portfolio of small nonfiction titles that create leverage, audience growth, and long-term income.

Stop the endless loop of document reviews and start getting clear approvals. This professional guide provides a simple, battle-tested framework for project managers who want to cut through the noise and get to a firm decision, every time.

Is There a Six-Figure Freelance Developer in You?Leaving the Rat Race with Python shows you how to nurture, grow, and harness your work-from-home coding business online --- and live the good life!It's an insider's guide to freelance developing. Start your new thriving coding business now!

The most truly passive online business I know is Amazon KDP. If you want to stay anonymous and build a passive online business system the hard way, this book is for you.

Unleash the full power of ChatGPT in your life and work with "ChatGPT: the AI Appliance". Dive deep into the AI that's changing the digital landscape and learn how the "black box" mindset can help you with prompt engineering, response evaluation, and more. Turn the "black box" into your personal toolkit for success!

Volume 4 of the Experiential Learning Series provides many tried-and-true experiential exercises, ready to use or modify for lessons in programming, testing, analysis and design, as well as personal development, team building, and organizational change.

This third volume of the series on experiential learning concerns itself principally with creating those experiences that simulate some life situation. This volume focuses on those simulation exercises, providing lots of examples and variations, using all the senses and all parts of the brain.

Designed to demonstrate how to debrief educational experiences, Experiential Learning 2 : Invention is "a gold mine" of questions and exercises useful for conducting retrospectives of on-the-job work. What could be more eductational than on-the-job work?

Your manager calls you into a meeting. Everyone is cheerful. The Head of Sales is there. “We heard AI is really useful for boosting productivity. We’d love to explore how it can help us in Sales.” Your manager agrees. “This is the perfect pilot. Please give us a concept until next week so we can see what we can do.”The follow-up arrives quickly. Sales is overloaded. Everyone is busy serving customers. Nobody has time for a workshop, but they do have Sales.xlsx. That is what you get. The clock is ticking.And then you open Sales.xlsx.You have nothing. No requirements, no workflows, not even a proper data model. What you have is a snapshot of the messy reality Sales is dealing with every day. Customer names, contacts, opportunity values, dates, products, notes, colors that apparently mean something, and a field called Status_Final_2, which tells you enough about its history that asking about Status_Final_1 would probably ruin the afternoon.And then it strikes you: they are not even using the CRM. You do not know why. Nothing you know from IT seems to apply in Sales, and they do not particularly care. They have customers to serve. Whatever happened between the shiny CRM rollout and this spreadsheet happened years ago. Today, Sales.xlsx is the truth.And now, just because AI promises productivity, they are asking for the benefits.How is that even supposed to work?

Turn one real skill into a scoped, priced, and launch-ready micro-offer. Includes a concise 13-page field manual and a 24-page workbook with 21 practical worksheets—without fake proof or income promises.

Why do technically successful projects still leave customers indifferent? The End of “So What?” shows how three simple principles—clarity, visible ownership, and focus—help professional-services teams turn competent delivery into trust, confidence, and customer delight.

Move beyond individual prompts and engineer the full information environment an AI model receives. Learn to design, test, measure, and govern context for reliable AI workflows.

David leaned forward, intrigued. "An inventor of better systems? You always say stuff like that Alex! What do you mean?""I have a theory. The reason most organisations fall back," Alex explained, "is because they fall into the trap of believing that they have 'arrived'. They find a process that works, a method they like, a 'way of working', and then they try to preserve it, to freeze it in time. They codify it, they build policies around it, and they turn it into a dogma. This approach may have worked in the past but in today’s fact paced world, a process which can’t be continuously improved, soon becomes an obstacle.”

"The AI confirmed X for me" used as proof of X. Outputs that sound brilliant but don't hold up to a severe re-reading. A three-page "AI policy" that nobody reads. Sound familiar? Thinking with LLMs, the Right Way is the system of critical thinking applied to LLMs: the Thinking-With Triangle (Intent / Adversary / Editor), the four meta-decisions of governance, the Socratic and adversarial practices for investigating and verifying. Not prompt engineering: the method for not letting yourself be mirrored.