Sharing a build takeaway that surprised me. I set out to make a gaming hint assistant (you screenshot where you’re stuck and it tells you what to do), and assumed the model quality would be the whole battle. It wasn’t. A capable vision model will happily tell you the boss’s second-phase attack, the plot twist three hours ahead, and the optimal build — all unprompted. For a spoiler-free hint tool, “helpful and complete” is actually the failure mode. Three things that moved the needle:
- State detection before answering. Instead of “what should I do here,” the first pass is “where in the game is this player, roughly how far along.” Grounding the response in the player’s likely progress stops it from referencing content they haven’t reached.
- A reasoning cap in the prompt. Explicitly bounding how far ahead the model is allowed to reason (“answer only the immediate obstacle; do not reference future areas, bosses, or story beats”) cut spoiler leakage far more than any post-hoc filter I tried.
- Screenshot > text query. Users describe their situation with spoilery words (“how do I beat the final boss”). A screenshot of the current screen is a much cleaner, lower-spoiler signal of where they actually are. It runs as a PWA so the hint shows on a phone/second screen instead of an overlay. Happy to talk more about the spoiler-guarding prompt design in the comments — curious if others building consumer LLM tools have hit this “too helpful” problem in other domains. submitted by /u/fatalgeck0
Originally posted by u/fatalgeck0 on r/ArtificialInteligence
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