Prompt Library

ChatGPT Prompts for Grant Writing

30 copy-paste prompts

30 tested prompts to draft letters of inquiry, needs statements, project narratives, measurable objectives, budgets, and funder-tailored proposals that win funding.

In short: This page contains 30 copy-paste ready prompts, organized into 6 categories with a description and pro tip for each. The first 5 prompts are free instantly, no signup needed. Drafted with AI writing assistance and checked against our editorial policy.

Louis Corneloup
By Louis Corneloup · Founder, Techpresso
Last updated ·Drafted with AI assistance (how we make prompts)

Letters of Inquiry & Concept Notes

5 prompts

Draft a Letter of Inquiry

1/30

✨ What it does

ChatGPT drafts a one-page letter of inquiry for [ORGANIZATION] to [FUNDER] about [PROJECT] and [$AMOUNT], aligned to [PASTE FUNDER PRIORITIES]. You personalize the opening, then send only after a human proofread.

<context> Organization: [ORGANIZATION] Mission: [ONE-SENTENCE MISSION] Funder: [FUNDER] Project: [PROJECT] Amount requested: [$AMOUNT] Funder priorities (from their site/guidelines): [PASTE FUNDER PRIORITIES] </context> <task> Write a one-page letter of inquiry (LOI) under 450 words. Structure it as: 1. Opening hook tying [ORGANIZATION]'s work to [FUNDER]'s stated priorities. 2. The problem [PROJECT] addresses, with one striking data point. 3. Our proposed solution and why we are credible to deliver it. 4. The specific amount requested and what it funds. 5. A closing that invites a full proposal. Use a confident, concise tone. Avoid jargon. Flag any place I should insert a local statistic in [BRACKETS]. </task>

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Pro tip: Paste the funder's actual guidelines into the context block so the mirror language is exact, not generic.

Write a Concept Note

2/30

✨ What it does

ChatGPT writes a two-page concept note for [PROJECT] serving [WHO YOU SERVE] over [MONTHS/YEARS], including the ask of [$AMOUNT]. You check every figure, then attach the note to the first outreach.

<context> Organization: [ORGANIZATION] Project: [PROJECT] Target population: [WHO YOU SERVE] Requested amount: [$AMOUNT] Project duration: [MONTHS/YEARS] </context> <task> Draft a 2-page concept note with these sections: Background, Problem Statement, Goal & Objectives, Approach/Activities, Expected Outcomes, and Budget Summary. Keep each section to 2-4 sentences. Make objectives measurable. End with a single sentence on organizational capacity. Output with clear section headers. </task>

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Pro tip: Reuse the concept note as the skeleton for a full proposal once a funder expresses interest.

Tailor an LOI to Multiple Funders

3/30

✨ What it does

ChatGPT spins [PASTE YOUR BASE LOI] into three funder-specific letters that keep the same facts while matching each [PASTE] priority set. You compare the three, then send only the version that still tells the truth.

<context> Master LOI: [PASTE YOUR BASE LOI] Funder A priorities: [PASTE] Funder B priorities: [PASTE] Funder C priorities: [PASTE] </context> <task> Produce three versions of the LOI, one per funder. For each, keep the core project facts identical but: 1. Rewrite the opening to echo that funder's specific language and focus areas. 2. Reorder the proof points so the most relevant one to that funder leads. 3. Adjust the closing ask to match their typical grant range if I provided it. Label each version clearly and note in one line what you changed and why. </task>

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Pro tip: Ask ChatGPT to keep a shared "facts" block so numbers never drift between versions.

Craft an Opening Hook

4/30

✨ What it does

ChatGPT writes five different opening paragraphs for a grant inquiry on [PROJECT] and [THE PROBLEM], aimed at [FUNDER]. You pick one hook, then paste it at the top of your letter and cut the rest.

<context> Project: [PROJECT] Problem: [THE PROBLEM] Funder: [FUNDER] Most compelling statistic or story I have: [PASTE] </context> <task> Write five distinct opening paragraphs (3-4 sentences each) for a grant inquiry. Use a different technique for each: (1) a startling statistic, (2) a single beneficiary story, (3) a provocative question, (4) a shared-values statement aligned to [FUNDER], (5) a moment-in-time urgency frame. After each, add a one-line note on when this opening works best. </task>

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Pro tip: Test the statistic hook on data-driven foundations and the story hook on community funders.

Summarize Funder Guidelines Into a Checklist

5/30

✨ What it does

ChatGPT turns [PASTE FULL RFP / GUIDELINES] from [FUNDER] into a submission checklist plus hard eligibility rules. You tick every required file, then stop if a rule knocks you out.

<context> Funder: [FUNDER] Guidelines text: [PASTE FULL RFP / GUIDELINES] </context> <task> Read the guidelines and produce: 1. A submission checklist (every required document, attachment, and form). 2. Hard eligibility criteria as yes/no questions I can self-assess against. 3. All stated word/page/budget limits. 4. The exact evaluation criteria and their weights if listed. 5. The deadline and submission method. Flag anything ambiguous that I should confirm with the program officer. </task>

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Pro tip: Run this first on any new RFP so you never get disqualified on a technicality.

Needs & Problem Statements

5 prompts

Write a Needs Statement

6/30

✨ What it does

ChatGPT drafts a 300 to 400 word needs statement for [ORGANIZATION] on [THE PROBLEM] in [GEOGRAPHY / POPULATION], using [PASTE STATISTICS, SOURCES]. You verify each source, then drop the statement into the narrative.

<context> Organization: [ORGANIZATION] Community served: [GEOGRAPHY / POPULATION] Problem: [THE PROBLEM] Data I have: [PASTE STATISTICS, SOURCES] Project: [PROJECT] </context> <task> Draft a needs statement of 300-400 words that: 1. Defines the problem with cited data, scaling from national to the local population we serve. 2. Explains the consequences of the problem going unaddressed. 3. Identifies the gap in current services (without disparaging other organizations). 4. Positions [PROJECT] as the logical response. Keep it evidence-led, not emotional. Mark every place that needs a citation with [SOURCE]. </task>

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Pro tip: Always replace every [SOURCE] tag with a real, dated citation before submitting.

Strengthen a Weak Problem Statement

7/30

✨ What it does

ChatGPT critiques and rewrites [PASTE] against specificity, data, local relevance, and flow, using [PASTE FEEDBACK] if you have it. You accept the sharper sentences, then put the rewrite in the proposal.

<context> Current problem statement: [PASTE] What reviewers said (if any): [PASTE FEEDBACK] </context> <task> Critique my problem statement against these criteria: specificity, data support, local relevance, logical flow, and absence of solution-bias. For each criterion, give a score out of 5 and one concrete fix. Then rewrite the statement applying every fix. Keep my real facts; do not invent statistics, but mark where stronger data would help with [ADD DATA]. </task>

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Pro tip: Never let ChatGPT fabricate figures, so the [ADD DATA] flag forces you to source real numbers.

Translate Data Into a Compelling Narrative

8/30

✨ What it does

ChatGPT turns [PASTE STATISTICS / SURVEY RESULTS] into two or three narrative paragraphs for a [FUNDER / REVIEW PANEL] that make the scale felt. You keep every number honest, then place the passage in the needs section.

<context> Raw data: [PASTE STATISTICS / SURVEY RESULTS] Audience: [FUNDER / REVIEW PANEL] </context> <task> Turn this raw data into 2-3 narrative paragraphs that make the scale of the problem felt, not just stated. For each key number: 1. Translate it into a human-scale comparison. 2. Connect it to a real consequence for [ORGANIZATION]'s population. 3. Keep every figure exactly as I provided it; do not round or alter. End with one sentence that frames why action is urgent now. </task>

💡

Pro tip: Ask for "human-scale comparisons" so a big number lands emotionally without exaggeration.

Build a Root-Cause Analysis

9/30

✨ What it does

ChatGPT maps [THE PROBLEM] for [WHO] from [PASTE NOTES] down to root causes your [PROJECT] can actually target. You drop symptom-only claims, then use the cause list in the needs section.

<context> Problem: [THE PROBLEM] Population: [WHO] What I know about causes: [PASTE NOTES] </context> <task> Develop a root-cause analysis I can use in the needs section. Provide: 1. The presenting problem (what people see). 2. 3-5 underlying root causes, each with a one-line rationale. 3. Which root cause(s) [PROJECT] directly targets. 4. A short paragraph explaining why addressing the root cause beats treating the symptom. Note any cause that I should validate with local data before claiming it. </task>

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Pro tip: Funders favor projects that hit root causes, so explicitly name which one your project targets.

Localize a National Problem

10/30

✨ What it does

ChatGPT bridges national [PROBLEM] data in [PASTE] to your [GEOGRAPHY / POPULATION], and points to local sources when you have [PASTE OR "none yet"]. You add the local numbers you can find, then cut any national claim you cannot localize.

<context> National/global problem: [PROBLEM] National statistics: [PASTE] Our service area: [GEOGRAPHY / POPULATION] Local data I have: [PASTE OR "none yet"] </context> <task> Write a passage that bridges the national problem to our specific community. Structure it as: national framing, then the funnel to local relevance. Where I lack local data, suggest exactly which local sources to pull from (e.g., census tract data, school district reports, county health rankings) and what metric to look for. Do not invent local numbers. </task>

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Pro tip: County health rankings and census tract data are credible, free sources reviewers trust.

Project Narrative & Methodology

5 prompts

Draft the Project Narrative

11/30

✨ What it does

ChatGPT writes a 600 to 800 word project narrative for [PROJECT] tied to [PROJECT GOAL], [PASTE LIST], [DURATION], and [WHO DELIVERS IT]. You assign real names to the staffing, then trim anything you cannot staff.

<context> Project: [PROJECT] Goal: [PROJECT GOAL] Key activities: [PASTE LIST] Timeline: [DURATION] Staffing: [WHO DELIVERS IT] </context> <task> Write a project narrative of 600-800 words covering: project overview, who it serves, the core activities in logical sequence, the implementation timeline, the team responsible, and how activities lead to outcomes. Use confident present/future tense ("we will"), tie each activity to an objective, and keep paragraphs short. Add subheadings. </task>

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Pro tip: Match the narrative's word count to the funder's stated page limit before you start editing.

Describe Your Methodology

12/30

✨ What it does

ChatGPT writes a methodology section that explains how you will deliver [PROJECT] using [EVIDENCE-BASED MODEL OR APPROACH] and [RATIONALE]. You add the citations you actually have, then delete methods you will not run.

<context> Project: [PROJECT] Approach/model: [EVIDENCE-BASED MODEL OR APPROACH] Why this approach: [RATIONALE] </context> <task> Write a methodology section that explains HOW we will deliver [PROJECT]. Include: 1. The model or framework we use and the evidence behind it. 2. The step-by-step delivery process. 3. Why this approach fits our population better than alternatives. 4. How we will adapt if early results lag. Cite the evidence base generically and mark exact citations needed with [SOURCE]. </task>

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Pro tip: Reviewers reward evidence-based models, so name the specific framework rather than "best practices."

Build an Implementation Timeline

13/30

✨ What it does

ChatGPT builds an implementation table for [PROJECT] over [TOTAL MONTHS] from [PASTE LIST], with phases, owners, and milestones. You put real names in the owner column, then slip the table into the narrative.

<context> Project: [PROJECT] Duration: [TOTAL MONTHS] Key activities: [PASTE LIST] </context> <task> Create an implementation timeline as a table with columns: Phase, Months, Key Activities, Responsible Party, Milestone/Deliverable. Break the project into 3-4 logical phases. Make sure every activity I listed appears, and add a launch and a close-out phase. Keep milestones concrete and verifiable. </task>

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Pro tip: Concrete milestones double as your progress-report checkpoints later, so make them measurable.

Write a Logic Model

14/30

✨ What it does

ChatGPT produces a logic-model table for [PROJECT] from [STAFF, FUNDING, PARTNERS], [PASTE] activities, and [WHAT SHOULD IMPROVE], plus a short theory-of-change. You check that each activity has an outcome, then paste the table into the application.

<context> Project: [PROJECT] Resources available: [STAFF, FUNDING, PARTNERS] Activities: [PASTE] Intended change: [WHAT SHOULD IMPROVE] </context> <task> Produce a logic model as a table with these columns: Inputs, Activities, Outputs, Short-Term Outcomes, Long-Term Outcomes/Impact. Keep outputs countable (numbers served, sessions delivered) and outcomes about change (knowledge, behavior, condition). Below the table, add a 3-sentence "theory of change" narrative connecting them. </task>

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Pro tip: Keep outputs (counts) and outcomes (change) strictly separate, which is where many drafts blur.

Demonstrate Organizational Capacity

15/30

✨ What it does

ChatGPT writes a capacity statement for [ORGANIZATION] using [N] years, [PASTE METRICS / OUTCOMES], and your staff and partners. You confirm every past result, then attach the statement where the form asks why you can deliver [PROJECT].

<context> Organization: [ORGANIZATION] Years operating: [N] Relevant past results: [PASTE METRICS / OUTCOMES] Key staff and qualifications: [PASTE] Partnerships: [PASTE] </context> <task> Write a capacity statement (250-350 words) proving [ORGANIZATION] can deliver [PROJECT]. Cover: track record with quantified past results, relevant staff expertise, infrastructure/systems, and strategic partnerships. Lead with the strongest proof. Be specific and confident without overclaiming. Mark any metric I should verify with [CONFIRM]. </task>

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Pro tip: Lead with a quantified past result; "served 1,200 youth, 87% completed" beats adjectives.

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Goals, Objectives & Evaluation

5 prompts

Write SMART Objectives

16/30

✨ What it does

ChatGPT writes three to five SMART objectives for [PROJECT] under [GOAL], for [WHO] in [DURATION], each with a measure and a source. You lock the numbers you can actually track, then drop those objectives into the evaluation section.

<context> Project goal: [GOAL] Project: [PROJECT] Population: [WHO] Timeframe: [DURATION] </context> <task> Write 3-5 SMART objectives for [PROJECT]. Each objective must be Specific, Measurable, Achievable, Relevant, and Time-bound, and follow the format: "By [date], [target number/%] of [population] will [measurable change], as measured by [data source]." After each objective, add a one-line note confirming which SMART criterion is strongest and which I should double-check for realism. </task>

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Pro tip: If you cannot name the measurement source after "as measured by," the objective is not yet measurable.

Separate Goals From Objectives

17/30

✨ What it does

ChatGPT sorts [PASTE] into true goals, measurable objectives, and activities so the levels no longer blur. You rewrite the mixed lines, then use the clean hierarchy in the form.

<context> My current goals/objectives draft: [PASTE] </context> <task> Review my draft and: 1. Identify which statements are true goals (broad, aspirational, the big change) versus objectives (specific, measurable steps). 2. Rewrite into a clean hierarchy: one or two goals, each with 2-3 supporting measurable objectives. 3. Flag any objective that is actually an activity in disguise and rewrite it as an outcome. </task>

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Pro tip: Activities ("hold 12 workshops") are not objectives; the objective is the change those workshops produce.

Design an Evaluation Plan

18/30

✨ What it does

ChatGPT designs an evaluation table for [PROJECT] that maps [PASTE YOUR OBJECTIVES] to indicators and methods you can run with [WHAT YOU CAN REALISTICALLY COLLECT]. You delete any indicator you cannot collect, then keep the rest as your plan.

<context> Project: [PROJECT] Objectives: [PASTE YOUR OBJECTIVES] Data capacity: [WHAT YOU CAN REALISTICALLY COLLECT] </context> <task> Design an evaluation plan as a table mapping each objective to: Indicator, Data Source, Collection Method, Frequency, and Responsible Party. Distinguish process measures (was it delivered) from outcome measures (did it work). Recommend one simple, low-cost method per indicator given my stated data capacity. Add a 2-sentence note on how findings feed back into the program. </task>

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Pro tip: Pick collection methods you can actually sustain; an over-promised eval plan undercuts credibility.

Define Outcomes and Indicators

19/30

✨ What it does

ChatGPT proposes outcome statements for [PROJECT] on [WHAT IMPROVES] for [WHO], each with quantitative and qualitative indicators. You set targets you can defend, then paste the pairs into the evaluation narrative.

<context> Project: [PROJECT] Intended change: [WHAT IMPROVES] Population: [WHO] </context> <task> For [PROJECT], propose: 1. 2-3 outcome statements (the change we expect in the population). 2. For each outcome, 1-2 quantitative and 1 qualitative indicator. 3. A realistic baseline-and-target approach for each indicator. Keep indicators measurable and tied to the outcome, not the activity. Note which indicators a funder is most likely to want reported. </task>

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Pro tip: Pair every quantitative indicator with one qualitative one; stories make the numbers persuasive.

Plan for Sustainability

20/30

✨ What it does

ChatGPT drafts a 200 to 300 word sustainability section for [PROJECT] after [DURATION], using [PASTE] revenue ideas for [ORGANIZATION]. You name only sources you will actually pursue, then place the section where the form asks what happens after the grant.

<context> Organization: [ORGANIZATION] Project: [PROJECT] Grant period: [DURATION] Current/potential revenue sources: [PASTE] </context> <task> Write a sustainability section (200-300 words) explaining how [PROJECT] continues after the grant ends. Address: diversified funding strategy, earned revenue or partnerships if relevant, capacity built that outlasts the grant, and any plan to institutionalize the work. Be realistic; do not promise permanent funding you cannot show a path to. Flag claims I must back up with [EVIDENCE]. </task>

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Pro tip: Funders fear being a one-time crutch, so emphasize diversification and capacity that outlasts the grant.

Budgets & Budget Justifications

5 prompts

Build a Line-Item Budget

21/30

✨ What it does

ChatGPT builds a calculated line-item budget for [PROJECT] totaling [$AMOUNT] over [MONTHS/YEARS] from [PASTE STAFF, MATERIALS, TRAVEL, ETC.]. You check every line against the funder rules, then submit only the math you can document.

<context> Project: [PROJECT] Total request: [$AMOUNT] Duration: [MONTHS/YEARS] Known costs: [PASTE STAFF, MATERIALS, TRAVEL, ETC.] Funder restrictions: [PASTE ANY, e.g. no indirect over 10%] </context> <task> Build a line-item budget as a table with columns: Category, Line Item, Calculation, Amount, Funding Source (this grant vs. other). Group into Personnel, Fringe, Supplies, Travel, Other Direct Costs, and Indirect. Show the math in the Calculation column. Total it and confirm it equals [$AMOUNT]. Respect every funder restriction I listed and flag anything that exceeds a stated cap. </task>

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Pro tip: Show the math in a calculation column; reviewers distrust round numbers with no formula behind them.

Write a Budget Justification

22/30

✨ What it does

ChatGPT writes a budget justification from [PASTE LINE-ITEM BUDGET] that ties each category to work on [PROJECT]. You cut any line you cannot explain, then attach the narrative to the spreadsheet.

<context> Budget: [PASTE LINE-ITEM BUDGET] Project: [PROJECT] </context> <task> Write a budget narrative/justification that explains WHY each line item is necessary for [PROJECT]. For every category: 1. State what it funds and how it connects to a specific project activity or objective. 2. Show the calculation basis (rate x quantity x time). 3. Justify any item a reviewer might question. Keep each justification to 2-3 sentences. Use the same category order as the budget. </task>

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Pro tip: Tie each cost to an objective; an unexplained line item is the fastest way to lose budget points.

Calculate Personnel Costs and Fringe

23/30

✨ What it does

ChatGPT calculates charged salary and fringe for each role from [TITLE, ANNUAL SALARY, % EFFORT for each], a fringe of [%], and [MONTHS]. You match the totals to payroll, then drop the figures into the budget form.

<context> Roles on the grant: [TITLE, ANNUAL SALARY, % EFFORT for each] Fringe rate: [%] Grant duration: [MONTHS] </context> <task> For each role, calculate the charged salary (annual salary x % effort x portion of year on grant) and the fringe (charged salary x fringe rate). Present as a table: Role, Salary, % Effort, Months, Charged Salary, Fringe, Total. Sum personnel and fringe separately and combined. Show every calculation. Flag any role where % effort across all grants might exceed 100%. </task>

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Pro tip: Double-check that no staffer's combined effort across grants exceeds 100% before submitting.

Explain Indirect Costs

24/30

✨ What it does

ChatGPT explains in plain language how to apply your indirect rate of [%] to [$AMOUNT or "to be calculated"] under the funder's rule. You confirm the allowed base, then write the same number on the budget and in the narrative.

<context> Funder indirect policy: [PASTE, e.g. "10% de minimis" or "negotiated rate"] Our negotiated/de minimis rate: [%] Direct cost base: [$AMOUNT or "to be calculated"] </context> <task> Explain in 2-3 plain-language sentences what indirect costs cover for our organization. Then calculate the allowable indirect amount given the funder policy and our rate, showing the formula and the base it applies to. If the funder caps or disallows indirect, state how we will cover those real costs instead. Do not exceed the stated cap. </task>

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Pro tip: If a funder disallows indirect, name your match or in-kind plan so the real cost is visibly covered.

Demonstrate Cost-Effectiveness

25/30

✨ What it does

ChatGPT writes a short cost-effectiveness argument for [PROJECT] using [$AMOUNT], [NUMBER] people served, and [PASTE] expected outcomes. You keep the comparison honest, then place the paragraph near the budget.

<context> Project: [PROJECT] Total cost: [$AMOUNT] People served: [NUMBER] Expected outcomes: [PASTE] Comparison/alternative cost if known: [PASTE OR "none"] </context> <task> Write a short cost-effectiveness argument. Calculate cost-per-participant and, where possible, cost-per-outcome. Frame these figures favorably and honestly. If I provided a comparison, contrast our cost against the alternative or the cost of inaction. Keep it to one tight paragraph plus the key figures. Do not invent comparison data. </task>

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Pro tip: Cost-of-inaction comparisons (e.g., cost of a dropout vs. the program) are powerful when you have the data.

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Funder Research & Tailoring

5 prompts

Profile a Prospective Funder

26/30

✨ What it does

ChatGPT builds a funder profile for [FUNDER] from [PASTE OR SUMMARIZE], with a fit score against [PROJECT] and open questions. You answer those questions from public records, then decide whether to invest the full proposal.

<context> Funder: [FUNDER] Info I have (990s, website, past grants): [PASTE OR SUMMARIZE] Our project: [PROJECT] </context> <task> From the information I provide, build a funder profile covering: stated priorities and focus areas, typical grant size and range, geographic scope, application process and deadlines, and recent grantees or patterns. Then assess fit with [PROJECT] on a 1-5 scale with reasoning, and list 3 questions I should answer before applying. Only use facts from what I pasted; mark gaps as [RESEARCH NEEDED]. </task>

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Pro tip: Feed it the funder's 990 and past-grants list; ChatGPT cannot browse, so it only knows what you paste.

Assess Funder Fit Before Applying

27/30

✨ What it does

ChatGPT scores fit between [FUNDER] and [PROJECT] using [PASTE] priorities, typical [RANGE], [SCOPE], [$AMOUNT], and [GEOGRAPHY]. You take a low score as a skip, then only write the long application when the fit is real.

<context> Funder priorities: [PASTE] Funder typical grant size: [RANGE] Funder geography: [SCOPE] Our project: [PROJECT], requesting [$AMOUNT] in [GEOGRAPHY] </context> <task> Score the fit between [FUNDER] and our project across four dimensions: thematic alignment, grant-size match, geographic eligibility, and stage/type fit. Give each a 1-5 score with one sentence of reasoning. Give an overall go / no-go / clarify recommendation. If "clarify," list exactly what to confirm with the program officer first. </task>

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Pro tip: A grant-size mismatch is a common silent rejection, so weight that dimension heavily.

Draft Outreach to a Program Officer

28/30

✨ What it does

ChatGPT writes a short email to a program officer at [FUNDER] about [PROJECT] and [QUESTION / FIT CHECK], under 150 words, plus a call-request variant. You swap in [NAME or "unknown"], then send from [ORGANIZATION] only if the ask is specific.

<context> Funder: [FUNDER] Program officer name: [NAME or "unknown"] Our project: [PROJECT] What I want to ask: [QUESTION / FIT CHECK] </context> <task> Write a brief, professional email (under 150 words) to a program officer that: introduces [ORGANIZATION] in one line, states the specific reason for reaching out, asks 1-2 focused questions, and respects their time. Tone: warm, concise, no fundraising pitch. Provide a clear subject line. Offer a short alternative version that requests a 15-minute call instead. </task>

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Pro tip: Ask a genuine eligibility or priority question, not for money; the relationship comes before the ask.

Mirror Funder Language

29/30

✨ What it does

ChatGPT lists the repeated terms [FUNDER] uses in [PASTE], then rewrites [PASTE A NARRATIVE OR LOI SECTION] so your facts stay and the wording matches. You reject any phrase that changes the work, then paste the aligned section.

<context> Funder's own language (mission, priorities, RFP): [PASTE] My draft section: [PASTE A NARRATIVE OR LOI SECTION] </context> <task> Identify the 8-12 key terms, framings, and value-words [FUNDER] uses repeatedly. Then revise my draft to naturally adopt that vocabulary where it honestly fits, without changing any facts or overclaiming. Output: (1) the term list, (2) the revised draft, (3) a short note on any term I should NOT adopt because it would misrepresent our work. </task>

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Pro tip: Mirroring builds resonance, but never adopt a term that overstates what you actually do.

Repurpose One Proposal for a New Funder

30/30

✨ What it does

ChatGPT reshapes [PASTE OR SUMMARIZE KEY SECTIONS] for [FUNDER] using [PASTE] priorities and [WORD LIMITS, REQUIRED SECTIONS, ELIGIBILITY]. You cut to the new limits, then reread for any leftover claim that belonged to the old funder.

<context> Master proposal: [PASTE OR SUMMARIZE KEY SECTIONS] New funder: [FUNDER] with priorities: [PASTE] New funder requirements: [WORD LIMITS, REQUIRED SECTIONS, ELIGIBILITY] </context> <task> Adapt my existing proposal for [FUNDER]. Specifically: 1. Map my existing sections to this funder's required structure and flag missing sections. 2. Re-angle the framing toward this funder's priorities. 3. Trim or expand to hit their word/page limits. 4. List anything in my master proposal that should be removed because it does not fit this funder. Keep all factual data identical across versions. </task>

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Pro tip: Build a reusable master proposal once; tailoring it per funder is faster and more consistent than starting fresh.

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Frequently Asked Questions

ChatGPT can draft strong, well-structured sections fast, but funded proposals still depend on a real, fundable project and accurate local data. Use these prompts to produce a solid first draft, then add your verified statistics, organizational specifics, and a human edit. Reviewers fund credible organizations, not polished prose alone.
No. ChatGPT can hallucinate or misremember figures and citations, which is fatal in a grant. Every prompt here uses [SOURCE] and [ADD DATA] markers precisely so you replace placeholders with real, dated, verifiable sources. Always fact-check every number against the original source before submitting.
Keep a single "facts block" (names, numbers, dates, budget totals) and tell ChatGPT to treat it as fixed while only re-angling framing and language per funder. The tailoring prompts in this set are built to vary positioning without touching your underlying data. Spot-check that budget totals and beneficiary counts match across every version.
Most funders care about the quality, accuracy, and fundability of your proposal, not the tool used to draft it. Some federal or research funders have begun adding AI-use disclosures, so check each funder's guidelines. Either way, treat AI as a drafting assistant and ensure the final submission reflects your real work and voice.
ChatGPT excels at structure-heavy sections like logic models, timelines, budget justifications, and first drafts of narratives. Write or heavily edit the organizational story, community relationships, and any claim about results yourself, since those require lived knowledge. The strongest workflow is AI for scaffolding, you for substance and verification.

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