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A note before we begin: This is the third lesson in a four-week experiment. I am doing every lesson alongside you, on the same tools, with the same constraints. Some days will land cleanly. Some will lead to dead ends and need rework. We’ll figure out what works, together.
If you are catching up, here’s what came before today’s lesson:
Intro: Build Your Own Investment Idea Engine
Lesson 1: How LLMs Work, and How to Defend Against Hallucinations
Lesson 2: Prompt Patterns That Outperform Casual Prompting
We are building an idea engine, so the next step is to make our workflow agent-friendly and our outputs machine-readable. This way, we can reuse them, validate them, and eventually automate them.
Why This Matters for Investors
When we evaluate investment ideas, we accumulate small facts and judgments: what the business does, why it surfaced, what could break the thesis, what we need to verify next. If those notes live only as free-form prose, they do not travel well. We cannot reliably sort, compare, or feed them into a sheet, dashboard, or follow-up workflow.
Agents and structured outputs are how we close that gap. An agent is simply a model that can use tools, read and write files, and show its work. Structured output is what makes the result reusable, not just readable.
Let’s launch into today’s lesson.
