Problem
Generating a “professional” post with an LLM from a slogan invents biography. The flow must interview, identify gaps, prepare the briefing, draft, and export — and refuse what was not confirmed.
Constraints
Python core with boundaries between interview, generation, authorship preservation, segmentation, and persistence. Model calls in an isolated workspace, provider allowlist, and prompts as versioned contracts. Hybrid evaluation: LLM plus deterministic heuristics. Missing experience never becomes false lived experience.
Decision
Adaptive interviews, authorial briefing, storyboard, veto on fabricated content, SQLite prompt registry with rollback, textual interface, and React/Vite frontend to review phases. Markdown or SlideMark JSON export after evaluation.
Current state
Base with tests for interview, LLM isolation, registry, persistence, and SlideMark conversion.
Limitations
It is not a generic thought-leadership generator. Without real repertoire, the system refuses; it does not complete the biography.