Beyond RLHF: Structural Coherence as a New Paradigm for AI Alignment

The framing here resonates. Making safety intrinsic to structure rather than layered on top after the fact is a more honest approach. Post-hoc correction assumes the underlying generative process is neutral, which it isn’t. The same logic applies at the prompt level. Most people write prompts as plain text and then wonder why the model drifts. Baking structure in at write time, role, constraints, reasoning steps as explicit semantic blocks, produces more coherent and predictable outputs. The model has less room to fill gaps with unintended behavior. I’ve been building flompt for exactly this, a visual prompt builder that decomposes prompts into 12 semantic blocks and compiles to Claude-optimized XML. Open-source: GitHub - Nyrok/flompt: flow + prompt = flompt - Visual AI Prompt Builder. Decompose, edit as flowchart, recompile into optimized machine-readable prompts · GitHub