How to use AI in grant writing without getting flagged
AI tools frequently strip out essential substance and replace it with generic boilerplate, yet even completely original drafts often trigger false positives on AI detectors. This leaves applicants trapped between producing low-quality text or facing unfair penalties for automation, while grantmakers simultaneously use the same opaque systems to evaluate submissions.
What people tried
Every workaround mentioned in the threads below. We haven’t tested any of them — and nobody here is claiming they worked.
- 1Using AI exclusively for non-creative administrative tasks like brainstorming, summarizing webpages, or outlining
- 2Refusing to use AI entirely out of ideological preference or fear of rejection
- 3Passing proposals through LLMs to improve grammar for non-native English speakers
In their words
Unedited, most upvoted first, each linked to the thread it came from.
“I understand why so many overworked nonprofit employees are using AI in the preparation of proposals. What concerns me significantly more is funders using AI to evaluate them.”source ↗
“We have seen issues where several applicants give similar responses to a question because they've all used AI, but it hasn't been a huge issue yet.”source ↗
“Most of the time, what comes out is terrible. The LLM removes much of the substance and replaces it with nonprofit brain rot”source ↗
“I’ve put my own 100% self written narratives into AI detection tools and it still says it's partial AI when none was used.”source ↗
Where this came up
People with this problem also raised
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- 3How to secure and support internal AI apps built by non-technical employees
- 2How to push back against forced AI tools at work
- 19Why does fixing AI mistakes take longer than doing it myself?