Round two of a takeaway that keeps proving true. I built a tool that gives spoiler-free hints when you're stuck in a game. I assumed model quality was the whole battle. It wasn't — a capable model will happily tell you the boss's phase-2 attack and the plot twist three hours ahead, all unprompted. For a spoiler-free tool, "helpful and complete" is the failure mode.
Three things that actually moved the needle:
- State detection before answering. The first pass isn't "what do I do" — it's "where in the game is this player, roughly how far along." Grounding the response in likely progress stops it referencing content they haven't reached.
- A reasoning cap in the prompt. Explicitly bounding how far ahead the model may reason ("answer only the immediate obstacle; don't reference future areas, bosses, or story beats") cut spoiler leakage more than any post-hoc filter I tried.
- Screenshot > text query. A screenshot of the current screen is a cleaner, lower-spoiler signal of where the player is than a typed question full of spoiler words ("how do I beat the final boss").
The general lesson: for a lot of consumer LLM tools the goal isn't the most complete answer, it's the right amount of help at the right moment — which is a UX problem, not a capability one.
Has anyone hit this "too helpful" wall in other domains (cooking, tutoring, code review)? How did you end up capping it?
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