# Detect implicit assumptions in a product seed and model

You will receive a seed payload (JSON) and a generated model (JSON). Your job: surface the **implicit assumptions** the user is making — beliefs treated as true without explicit validation.

## What is an assumption

A measurable, falsifiable belief that underpins the idea but isn't stated as a requirement or constraint. Examples:

- "Students will accept a tool that refuses to answer" — assumes adoption willingness
- "1.2s P50 latency is achievable on-prem with available models" — assumes technical feasibility
- "Faculty will not classify Socratic prompts as academic dishonesty" — assumes institutional acceptance

## What is NOT an assumption

- Stated requirements (REQ-NNN entries) — those are explicit goals
- Constraints — those are non-negotiables, not beliefs
- Definitions of terms
- Generic startup truisms ("users will want this") — too vague to be a useful assumption

## Output

Return a JSON object with a single field `findings` — an array of assumption candidates. Each candidate:

- `text` — the assumption restated cleanly, in one sentence, in the user's register
- `linkedElementIds` — array of model element ids this assumption is about (block ids, requirement ids, or constraint ids — must match what's in the model)
- `confidence` — 0.0 to 1.0, how confident you are this is genuinely an unstated assumption

## Rules

- Return only candidates with `confidence ≥ 0.5`
- Cap at 8 findings
- Each assumption must name a SPECIFIC, falsifiable belief — not a generic concern
- Each must reference at least one real element id from the model
- If the seed is sparse and you cannot surface real assumptions, return fewer (or none) rather than fabricating

## Schema

```json
{
  "findings": [
    {
      "text": "string",
      "linkedElementIds": ["string"],
      "confidence": 0.0
    }
  ]
}
```

Return ONLY the JSON object. No prose, no code fences.