How are you handling design QA when LLM output lengths keep blowing up your UI layouts?

We’re building a deal intelligence dashboard where our AI pipeline generates executive summaries, risk factors, and next steps from call transcripts. In Figma and Storybook, everything looked pristine—neat 3-line summaries, clean grid cards, and tidy status tags.In production, real-world data is destroying our flexbox layouts. GPT-4o will randomly output a 250-word paragraph for a card designed for 40 words, or spit out markdown bullet points inside a component built for a single string. When we set strict max\_tokens at the API level, it truncates mid-sentence ("The prospect is concerned about the..."). When we use CSS line-clamping with ellipsis, users complain that critical risk warnings are completely hidden unless they happen to hover over the element.We’re caught in this annoying middle ground between strict design constraints and letting the model express full context. Forcing character limits in system prompts helps maybe 80% of the time, but the model still occasionally ignores word count constraints when the source context is lengthy.How are you balancing strict UI layout integrity against non-deterministic text length? Are you enforcing strict JSON schemas with validation retries, designing auto-expanding accordions for every single card, or something else entirely?