> ## Documentation Index
> Fetch the complete documentation index at: https://docs.textql.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Module 1 · Define Your North Star

> The intro is the why. This is the doing: rather than fill out a form, you let Ana look at your actual data, ask the right scoping questions, and write the North Star down. That … (~15 min)

The intro is the *why*. This is the *doing*: rather than fill out a form, you let Ana look at your actual data, ask the right scoping questions, and write the North Star down. That single artifact (`north_star.md`) is what every later module builds toward.

Paste this prompt. Ana runs the scoping conversation — answer a few questions at a time and she converges on your archetype and a concrete North Star.

```text Prompt theme={null}
Help me define the North Star for our ontology before we build anything.
1. Look at the data connected to this thread and summarize in 3-4 lines what we have: the key tables, the grain, and the domains it covers.
2. Then ask me 5-7 sharp scoping questions to pin down what we are really doing - who will use this, what decision it changes on Monday morning, whether we are after BI parity / care-gap and quality / risk adjustment / operational queries, what "working" looks like in 30 days, and where our data is messiest. Ask a few at a time, not all at once.
3. From my answers plus the data, recommend the archetype that fits (A BI parity, B care-gap/quality, or C risk adjustment) and draft our North Star: one short paragraph on what this ontology is for, plus the 6-8 questions it must answer in 30 days.
4. Save it as north_star.md in the ontology and propose it as a reviewed change, so every later step builds toward it.
```

<Check>
  **You'll see:** **You will see:** Ana summarize your data, ask scoping questions, recommend an archetype with a reason, and draft a `north_star.md` — a paragraph plus the questions that define "done." Review it, push back, ratify.
</Check>

<Note>
  **Why a prompt, not a 50-question checklist** —

  No giant pre-written question bank that nobody maintains. Ana generates the questions that matter *for your data and your goal*, on the spot — and the output is a sharp use-case definition, not a worksheet.
</Note>

### ✅ Checkpoint

* [ ] You have a written `north_star.md` (purpose + the 6-8 questions it must answer)
* [ ] You picked an archetype (A / B / C), or Ana recommended one and you agreed
* [ ] It names something real that changes on Monday morning, not "model all of healthcare"