Join the Latticework AI Bootcamp and progress at your own pace. Participation is open to members and paid subscribers.
A note before we begin: This is the fourth lesson in a 16-lesson self-paced course. I did every lesson on the same tools, with the same constraints, that you will use. Some lessons will land cleanly. Some will lead to dead ends and need rework.
If you are catching up, here is what came before this 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.
