Went for AI training that only teaches you to prompt, or use a tool?

You want to get on top of AI risk and governance, but where do you even start? Search Google or Perplexity and you get more results than anyone can reasonably read - most of it noise, much of it vendor pitches. It is easy to spend hours and come away with nothing.

This book is the answer I keep giving when people ask. Six short reading lists of papers, guidelines, and articles - governance, data management, evaluation and testing, human oversight, third-party AI risk, and explainability - each entry with a note on why it is useful and worth your time. Organized in circles of progression: start at the center and work outwards. Read the one you need; nothing here assumes you read the others.

What you get: a clear, curated path through a noisy field, so you can start on the right things instead of drowning in search results. No affiliate links, no vendor pitches - just what is worth reading in each area, from someone who wrote a national set of AI risk guidelines and has read most of the rest. Work through it and you will understand AI risk better than most people paid to talk about it.

Not exhaustive, but a good place to start. You get the PDF to download and keep, free.

You sit through the workshops and the demos. Someone shows a clever prompt, everyone marvels at the output, and no one stops to ask what the model is actually doing - what data it is seeing, what task it is solving, why it works here but might not on the next example. So when the next model drops, you feel like you have to relearn everything. I call this prompt and pray, and it wears you out.

The fix is boring, and it lasts. Before reaching for a model, ask three questions: what data do I have, what task am I doing, which method fits. My PhD supervisor put it in three words - "Let's start with the task" - and years of working through data types and tasks showed me he was right. That is what this book teaches: how to see AI through the lens of data types and tasks.

Why it matters now, in the age of LLMs and agents: prompts are brittle, and a framework for thinking is more durable than a harness. AI has always been about data and tasks. LLMs wrap over those tasks in a more general way; agents are an LLM orchestrating tools and other LLMs on tasks. Strip the headline away and the data types and tasks are the same ones that were there ten years ago.

What you get: the book works through the main data types - tabular, text, image, networks, and time series - the same way each time. What the data is, how you break it down, how AI works on it. From the current landscape to the frontier. Learn the pattern once and you can look at any new tool or paper and understand what it is doing, instead of relearning the hype every six months.

If you are here for the frontier, you are in the wrong place. This is deliberately about the boring fundamentals - the part of AI that does not expire when the next model drops. You get the PDF to download and keep.

You have twenty frameworks to comply with, two hundred use cases to govern, and a control checklist a thousand questions long for each. Multiply it out and you get millions of boxes. Tick them all and you still are not governed - you are just very busy. Governance that grows faster than the AI does not scale, and most of what passes for AI governance today is exactly that.

This short book compiles some thoughts on three moves that collapse twenty frameworks and hundreds of use cases into one backbone you can actually manage - and scale.

Govern to a common spine. Strip the packaging off twenty frameworks and they ask the same questions. Build one backbone and twenty columns collapse to one.

Govern with the unit that scales. "What are the use cases?" is the wrong first question - a use case is the least scalable thing to govern. Underneath sit units that repeat. Govern those once and every use case inherits.

Govern as a system, not a checklist. A thousand-question checklist passed box by box still fails, because the boxes do not talk to each other.

Some ideas on how to keep control of AI as the number of systems climbs - in a firm, a regulator, or a team of one - without a pricey consultant and without drowning in boxes. It is deliberately boring, because the parts that last usually are. I wrote Singapore's first AI risk management guidelines for the financial sector, and much of this is what I wish I could have said in the margins of them.

You get the PDF to download and keep.

Open ChatGPT, Claude, or Gemini and ask it to build you a low-volatility portfolio. You will get a confident, reasonable-sounding answer. But did it actually check the volatility? Calculate whether the allocation can hit your target? Verify the correlations? Maybe. And maybe it hallucinated the whole thing and told you with complete confidence. You cannot tell which - and if this powers your real portfolio, that is a serious problem.

This is not a bug in any one model. It is how LLMs work: they are good at language, not at being databases or calculators. Real finance questions are multi-step - fetch the data, calculate the metrics, check the correlations, optimize the allocation - and an LLM on its own cannot do them reliably. Unless you give it tools. That is what this book is about.

What you get: four patterns - tool calling, ReAct, CodeAct, and orchestration - that ground an LLM in real data and real calculations, each explained plainly and with working code you run yourself in Google Colab. A finance primer if you want the concepts underneath the metrics. An n8n bonus chapter that builds the same patterns as visual workflows, with no Python. By the end you will have built a working portfolio assistant that fetches real prices, calculates real metrics like volatility, Sharpe ratio, and value at risk, shows its reasoning at each step, and answers follow-ups about your holdings.

It gets you from "LLMs hallucinate" to "I have a working tool I can trust." It covers analysis, not execution, and stops short of production deployment - that is a bigger next step. Helpful but not required: basic Python, some APIs, some finance. Not required: ML expertise or any prior agent experience. If you can read, you have enough to start. The reason it works is not that the model is smarter, but that the architecture is right.

You get the PDF to download and keep, along with the code.