44 lines
1.6 KiB
Markdown
44 lines
1.6 KiB
Markdown
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# Phase 2 Research
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## Question
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How should the diagnostic interview loop be implemented while preserving the
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Phase 1 typed workflow boundary and avoiding premature infrastructure?
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## Findings
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### In-memory persistence is enough for Phase 2
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The goal is to prove request/response flow and evidence preservation. A database
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would add migration, repository, and lifecycle complexity before the product loop
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is proven. An in-memory store with clear interface boundaries keeps the future
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database replacement straightforward.
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### Standard library routing remains sufficient
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The current backend already uses `http.ServeMux`. Phase 2 can add route patterns
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with path variables using Go 1.23's standard mux support. No router dependency
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is needed.
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### Deterministic grading is acceptable as a workflow stub
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Phase 2 requires typed grading through the workflow boundary. It does not require
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live LLM grading. A deterministic stub can grade on answer length and preserve
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evidence. This proves the product state flow and keeps live model integration
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for a later phase.
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### Keep interview domain separate from HTTP
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`internal/interview` should own session creation, question catalog selection,
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answer recording, and grade attachment. HTTP handlers should translate requests
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and responses only.
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## Recommendation
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1. Add `internal/interview` domain service and in-memory store.
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2. Add a small backend developer question catalog.
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3. Add typed endpoints for creating/getting sessions and submitting answers.
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4. Extend workflow stub to return deterministic `GradedAnswer`.
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5. Add tests at domain and HTTP layers.
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6. Verify with `go test ./...`, OpenSpec, and line-count checks.
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