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A note before we begin: This is the fourth 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
Lesson 3: Tools, Agents, and Structured Output
A quick note before we begin: our goal today is not to become EDGAR power users in 30 minutes. It is to build a habit. When an AI-generated statement could change an investment conclusion, we verify it against the primary filing.
Why This Matters for Investors
If we only learn one data source in this bootcamp, it should be EDGAR, because every U.S.-listed company’s filings live there, free, official, and close to the source of truth. The models can summarize, compare, and extract, but EDGAR is where we verify.
In addition to EDGAR basics, I’ll share some slightly advanced tips for using EDGAR, and we’ll also take a quick look at similar databases in other countries, using Japan as an example.
Let’s launch into today’s lesson.
