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model-research/findings/2026-04-26-07-emerging-role-assignments-pattern-not.md
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Rodin 6af8a6ee10 refactor(findings): split ALL-FINDINGS.md into per-experiment files
Break the monolithic 3249-line findings file into 29 individual files,
one per experiment. Each file is named YYYY-MM-DD-NN-slug.md for easy
chronological sorting and discovery.

No content changes — purely structural reorganization.
2026-05-06 07:15:50 -07:00

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# Finding 7: Emerging role assignments (pattern, not conclusion)
**Date:** 2026-04-26 (one day of intensive work — treat as hypothesis)
- Opus (via Claude Code): complex generation needing deep project context.
Rich context: CLAUDE.md, full codebase access, design docs. Broad mandate.
- Sonnet: parallel volume work (5 subagents drafting simultaneously).
Rich context per section, constrained output scope.
- GPT-5: independent analytical review. Rich context (diff + files + issue).
Best when task is bounded and explicit.
- GPT-4.1 Mini: focused narrow analysis (bias detection). Minimal context,
precise question. Cheap and fast.
- **Takeaway:** The role assignment matters, but so does the context shape.
Opus gets broad context + broad mandate. Sonnet gets broad context +
narrow scope. GPT-5 gets rich context + explicit task. GPT-4.1 Mini gets
minimal context + laser question. We haven't tested swapping these
combinations — that's where the real learning will come from.