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A good prompt helps. It won't replace an understanding of how the system works or the checks needed before you let it act.
This book explains that machinery in four parts. How it actually works: the four approaches, how a language model produces text one token at a time, what training does and doesn't fix, the context window, and retrieval. Generative AI in finance: choosing the right tool per feature instead of per project, the uses that actually work, and the prompt and the chatbot built around it - including prompt injection and why the critical restrictions live outside the model. Agents: the loop and its harness, tools and least privilege, how agents fail across several steps even when each step looks fine, and the trajectory - what to record so you can reconstruct what the system did, in what order, and with whose authority. And making it safe to use: evals for answers and actions, guardrails, workable human review, and monitoring.
Along the way it names the terms you'll hear - prompt engineering, context engineering, the harness, evals, LLM-as-a-judge, human-in-the-loop - and shows what each one actually buys you. Finance examples throughout, from covenant summaries to payment agents. Thirty hand-drawn figures, the key lines bolded, no code.
It is written by the person who led AI risk supervision at the Monetary Authority of Singapore and wrote Singapore's AI risk management guidelines for the financial sector. You don't need to build a model to follow it. But you should come away able to ask a team what its system does, what could go wrong, and how it would know.
All of it is deliberately boring. That is the point. About eighty pages. A short primer is listed separately, if you want the tour first.
A good prompt helps. It won't replace an understanding of how the system works or the checks needed before you let it act.
This book explains that machinery in four parts. How it actually works: the four approaches, how a language model produces text one token at a time, what training does and doesn't fix, the context window, and retrieval. Generative AI in finance: choosing the right tool per feature instead of per project, the uses that actually work, and the prompt and the chatbot built around it - including prompt injection and why the critical restrictions live outside the model. Agents: the loop and its harness, tools and least privilege, how agents fail across several steps even when each step looks fine, and the trajectory - what to record so you can reconstruct what the system did, in what order, and with whose authority. And making it safe to use: evals for answers and actions, guardrails, workable human review, and monitoring.
Along the way it names the terms you'll hear - prompt engineering, context engineering, the harness, evals, LLM-as-a-judge, human-in-the-loop - and shows what each one actually buys you. Finance examples throughout, from covenant summaries to payment agents. Thirty hand-drawn figures, the key lines bolded, no code.
It is written by the person who led AI risk supervision at the Monetary Authority of Singapore and wrote Singapore's AI risk management guidelines for the financial sector. You don't need to build a model to follow it. But you should come away able to ask a team what its system does, what could go wrong, and how it would know.
All of it is deliberately boring. That is the point. About eighty pages. A short primer is listed separately, if you want the tour first.