Created a dataset system for training real LLM behaviors (not just prompts)

Most LLM dataset discussions still revolve around size, coverage, or “high-quality text,” but in practice the real failure mode shows up later when you actually plug models into workflows. Things like: tool calls breaking structured outputs drifting multi-step reasoning collapsing models losing grounding over longer runs We ran into this repeatedly while building LLM systems, and it became pretty clear