There’s a phrase I find myself repeating when teaching people how to use AI in their grant writing and operations, “data out is only as good as data in”. It sounds simple, but it is the single biggest factor that determines whether AI becomes a real capacity-builder—or a generator of unclear and inaccurate AI slop.
Producing Strong, Fundable Language with AI
Nonprofits work with complex histories, sensitive program data, and community-centered narratives. You can’t throw vague prompts or half-remembered program descriptions at an AI model and expect it to produce strong, fundable language. AI isn’t magic. It’s a mirror. It reflects whatever you feed it.
AI can elevate under-resourced nonprofits by strengthening writing quality, improving consistency, and preserving institutional memory.
Funders want specifics. Who do you help? How specifically do you do your work? What are your goals and objectives? What are your results? All of these questions should be addressed and the more precise you can be the better. It’s the difference between a generic paragraph and a proposal section that reads like it came from a well-resourced institution with a full development team.
Context is Key When Running AI Prompts
Before running a prompt, you should always think, “What context does the model need?”
If you ask, “Draft a needs statement,” and give no data, the output will be generic. Feed it your last three needs statements, your program data, and the target population, and suddenly the draft is aligned, specific, and credible. Your payload determines whether the model acts like an intern—or an executive director. Here are some of the documents we suggest training your AI on:
- Past Grants:
Both successful and unsuccessful applications. AI can detect patterns, preferred language, and funder expectations. - Best Practices in Grant Writing:
Scoring rubrics, logic model frameworks, evaluation standards, federal boilerplate language, and funder priorities.
- Program Data:
Enrollment, demographics, outputs, outcomes, and trends across years. - Evaluations and Reports:
External evaluator summaries, PHQ-9 data, success stories, performance metrics. - Client Stories:
Real narratives that ground the work in lived experience (de-identified when needed). - Organizational Assets:
Mission, vision, service area, partner profiles, strategic plan, staffing plans and organizational charts. - Institutional Memory:
Board decisions, program changes over time, partnerships, compliance learnings, lessons learned.
Training to be a Valuable Asset
AI can elevate under-resourced nonprofits by strengthening writing quality, improving consistency, and preserving institutional memory. But none of that happens automatically. The input determines the output. The payload determines the precision. The training materials determine whether AI becomes a liability or a true asset.
Interested in working together? We look forward to learning more about your organization and helping you build a stronger, more sustainable funding future. Contact Us
