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The Perfect Starting Prompt

Here's what I'd recommend someone type as their first message:

I want to build a claims triage model for my property & casualty insurance company. The model should read an incoming claim description (free-text from a policyholder or agent) and classify it into a triage tier — urgent, standard, or low-priority — while also extracting key fields like claim type (auto, home, liability), estimated severity, and whether fraud indicators are present.

The output should be structured JSON so it can feed directly into our claims management system. Here's an example of what a good input/output pair looks like:

Input: "My car was rear-ended at a stoplight on 6/12. The other driver fled the scene. Airbags deployed, I was taken to the ER by ambulance. Police report filed #2024-38291."

Output:

{
  "triage_tier": "urgent",
  "claim_type": "auto",
  "severity_estimate": "high",
  "fraud_indicators": false,
  "reasoning": "Bodily injury with ER visit, hit-and-run, police report present. Requires immediate adjuster assignment."
}

The users are claims intake specialists who currently do this manually. We process ~2,000 claims/week and want to auto-triage the obvious ones so adjusters focus on complex cases.

Edge cases I'm worried about: claims with multiple coverage types (e.g., auto + medical), vague descriptions with almost no detail, potential fraud red flags, and claims that reference prior open claims on the same policy.

Why This Prompt Works

It hits every dimension I'd need to build a strong end-to-end workflow without follow-up questions:

What Makes It Better Than a Vague Prompt

Compare it to what most people actually type:

"Help me build a claims triage model"

That 6-word version would require me to ask 5–6 rounds of clarifying questions about output format, claim types, triage logic, edge cases, and who the users are. The detailed version skips straight to building — I could go directly to setting up the acceptance rubric, picking models, and generating data.

Key Ingredients to Include

If someone's adapting this for their own use case, the formula is:

1. What the model does — the specific task, not just the domain
2. One concrete input → output example — worth more than a paragraph of description
3. Output format — structured (JSON/labels) vs. free-text, and what fields matter
4. Who uses it and how — shapes tone, complexity, and error handling
5. 3–5 edge cases you’re worried about — the hard cases define the model more than the easy ones
6. Why a fine-tuned model — helps me right-size the approach (small/fast vs. large/capable)