> ## 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. # Start with Your North Star > The single biggest mistake with a starter pack is to skip this step — to point it at your warehouse, decide it’s "close enough," and ship. That bakes in decisions before you kno… (~15 min) The single biggest mistake with a starter pack is to skip this step — to point it at your warehouse, decide it's "close enough," and ship. That bakes in decisions before you know which questions matter, and you get a model that's 10% useful and 90% noise. This module is the antidote, and it takes 15 minutes. ## 0.1 · Answer one question **The North Star question** — **When this works, what does someone do differently on Monday morning?** Not "model all of insurance." Something narrow and real: *"the actuary stops waiting a week for an accident-year loss-ratio cut by line,"* or *"the underwriting lead sees frequency and severity moving apart in time to re-rate."* If you can't name it yet, you're not ready to build the ontology — you're ready to have **this** conversation. Have it first. ## 0.2 · What a North Star looks like (examples to spark yours) Most P\&C / carrier teams land on a North Star adjacent to one of the three below. These are **examples, not a menu to pick from** — yours might be one of these, a blend, or something next to them. In **Module 1**, Ana drafts yours from your actual data; reading these now just tells you which surfaces and classification become relevant once it's set. (Full detail in the repo's [`NORTH_STAR.md`](https://github.com/TextQLLabs/ontology-starter-kits/blob/main/insurance/NORTH_STAR.md).)
ArchetypeForLead with
**A · Loss-ratio & reserving parity**"Match the actuarial numbers we already trust"`loss_ratio`, `combined_ratio`, `reserve_position` + golden queries (loss ratio **0.5469**, combined **0.7969**) — reconcile *first*, govern second.
**B · Underwriting & pricing**"See frequency and severity before the loss ratio does"`claim_frequency`, `claim_severity` + the frequency × severity ≈ pure-premium note + LOB/peril rollups.
**C · Claims operations**"Manage the open inventory and the reserve position"`reserve_position`, `claim_severity` + the grain discipline + the layered reserve vocabulary.
## 0.3 · You won't fill out a checklist — Ana drafts it There's no question bank to work through and no form to fill out. In **Module 1** you'll paste one prompt and Ana looks at your actual data, runs a short scoping conversation, recommends the archetype that fits, and writes your North Star down as `north_star.md` — a paragraph on what the ontology is *for* plus the 6–8 questions it must answer in 30 days. That single artifact is what every later module builds toward. The watch-outs that make you sound like the expert in the room — written vs. earned, paid vs. incurred, accident vs. calendar year — live in the repo's decision notes, and Ana surfaces them as they come up. **Why the model gets cheaper as you use it** — This isn't just good practice — it's the design. An agent that re-discovers your warehouse on every question pays an "amnesia tax" (most of its tokens go to rediscovery, and results aren't reproducible). Reading a known model first and committing what it learns inverts that: discovery is paid **once** and amortized across every future question. See TextQL's research, *"Malleability Is All You Need"* (VLDB 2026) — a measured \~30% token reduction and a model that gets more reliable the more it's used. ### ✅ Checkpoint * [ ] You can state your North Star in one sentence * [ ] You have a candidate archetype in mind (loss-ratio parity / underwriting / claims ops) — or you'll let Ana recommend one in Module 1 * [ ] You're ready to let Ana draft `north_star.md` from your data in Module 1 **Not sure which archetype?** That's fine — in **Module 1** Ana looks at your data and recommends one. You don't have to decide here.