know.

CookbookResearch and civic lifeNo. 42

A grant boilerplate libraryreusable sections for every application

Recipe No. 42Research and civic life

For
The development director of a small nonprofit who writes twenty applications a year alone
You bring
Last year’s strongest applications, the annual report, the audited budget, the evaluation data, a folder of signed letters of support, and the AI assistant you already use
You get
A private library of approved, dated sections — mission, programmes, outcomes, budget narratives, letters on file — and a first draft for each new funder assembled from it
Time
An evening for your assistant to take the old applications apart, two days of your review, then an hour for each new application and a morning for the annual refresh
Works in
know.sh on the web · your AI assistant, through MCP
Keep it
Private. This one never goes on a public link.

A development director at a small nonprofit answers the same questions twenty times a year, in twenty different word limits. Describe your organisation in 150 words. Describe the need. How do you measure outcomes? Last year’s answers are scattered across portal submissions and Word files named FINAL v3, and each one has a slightly different number of learners served.

This recipe keeps the approved language in one place. The shelf has five documents: Organisation, Programmes, Outcomes and data, Budget narratives and Letters of support. Each finding is one reusable block, in the length funders ask for, with the date it was last checked on its first line. Your assistant — Claude, ChatGPT or a local model — takes last year’s applications apart into those blocks; you approve each one in the editor. When a new funder’s guidelines arrive, it assembles a first draft from blocks you have already approved and says which ones are out of date.

The draft is where your work begins, not where it ends. You edit it, you check every number, and the application goes out under your name.

What you will use

Shelf
One shelf, Grant library, with a line naming the fiscal year the numbers are current to.
Research document
Five standing documents, plus one document per application that is assembled from them.
Finding
One block per finding, titled with its subject and length; descriptive blocks as Observation, numbers as Evidence, known gaps as Problem.
Your AI assistant
Takes past applications apart into blocks, drafts shorter versions, files a first draft for each new funder from the approved blocks, and checks the library for figures that disagree.
The editor
Where you choose the better answer, cut to length, check each figure and change the date line when a block is approved.
Revisions
Keeps last year’s version of every block through the annual refresh, and shows which lines an assistant wrote and which you changed.
Look up
Finds a block by the phrase a funder uses: capacity, sustainability, theory of change.

Method

  1. 1

    Have your assistant take last year’s applications apart

    Connect your assistant to know.sh once, following the support page. Give it the four or five applications that were funded and the annual report, and ask it to make a shelf called Grant library with five documents — Organisation, Programmes, Outcomes and data, Budget narratives, Letters of support — and to file every answer that recurs as a block: the mission, the history, the need, each programme, the evaluation method, the staffing, the budget narrative.

    Where two applications answer the same question differently, tell it to file both, side by side, and say which application each came from. Choosing is your job.

  2. 2

    Approve each block in the editor, at the lengths funders ask for

    Go through the shelf in Edit. Keep the better of each pair, merge where both have something, and delete the rest. Every finding is one block, titled with its subject and length: “Mission — 50 words”, “Mission — 150 words”, “Adult literacy programme — 250 words”, “Evaluation method — 500 words”. Portals count words and characters; ask your assistant to draft the 50- and 150-word versions from the one you approved, then cut them yourself.

    Put the date and the source on the first line of every approved block: “Last updated 14 August 2026, from the FY2026 evaluation report.” File descriptive blocks as Observation and blocks built on numbers as Evidence, with a link to the published annual report or evaluation in their sources where one exists.

  3. 3

    Keep the numbers in one place

    Put every figure a funder might ask for in Outcomes and data, one finding each: learners served, sessions held, reading-level gains, the waitlist, the cost per learner. Other blocks refer to these numbers rather than repeating them, so when a figure changes you change it once.

    Where you know the data is weak — no follow-up after learners leave, a small sample — say so in a Problem finding. Funders ask, and an honest answer written calmly in advance is better than one written at midnight before a deadline.

  4. 4

    Record where the letters of support are

    Letters of support are signed documents, and they belong in the shared drive where the signed copies live. Give your assistant the scanned letters and ask for a finding per letter in Letters of support, titled date first: “2026-04-02 — Lowbridge Public Library director”. The body says who wrote it and in what role, the application it was written for, what it says in two lines, and the folder where the signed copy is kept; add the folder yourself if it did not know.

    When a funder wants letters dated within the last year, a glance down the finding titles tells you whose to ask for again.

  5. 5

    Have your assistant assemble the first draft

    Paste the new funder’s questions and word limits into your assistant and ask it to draft an application from the library: a new document on the shelf, one finding per question, each built from existing blocks and naming the blocks it used.

    Ask it to flag any block last updated more than a year ago, any question the library cannot answer, and any place where it went beyond the blocks. Those flags are your to-do list.

  6. 6

    Edit the draft into your application

    Read the draft as you would a new staff member’s. Cut what does not fit this funder, add what only you know about why this programme suits their priorities, and check every number against Outcomes and data. The revisions of each finding show the assistant’s draft and your changes, one after the other.

    When it is right, copy each answer into the funder’s portal. Then look at what you rewrote: if a better answer to a common question emerged, save it back into the library as a new block, dated.

  7. 7

    Refresh the library once a year

    When the audit and the annual evaluation are done, give the library a morning. Update every figure in Outcomes and data, then ask your assistant to list blocks that still mention last year’s numbers or have not been updated in twelve months. Change the date line on each block you check, even when the words stay the same.

    Revisions keep last year’s version of every block. When a funder asks how this year compares with last, open a block’s revisions and read last year’s figures; there is no need to restore them.

Specimen

  1. You

    Here are the Marlow Community Foundation’s five questions for its spring 2027 cycle, with word limits. Using my shelf “Grant library” in know.sh, draft an application as a new document on that shelf, one finding per question. Build each answer from existing blocks and name the blocks you used. Flag any block older than twelve months and any question the library cannot answer. Do not invent figures.

  2. research_listGrant library: 5 documents, 64 findings
  3. search“evaluation”, “waitlist”, “sustainability”, “board”
  4. finding_getRead 14 blocks from Organisation, Programmes, Outcomes and data and Budget narratives
  5. research_createCreated Application — Marlow Community Foundation, spring 2027
  6. finding_addAdded five findings, one per question, to Application — Marlow Community Foundation, spring 2027

  7. ChatGPT

    The draft is on the shelf. Three things need you:

    • Question 3 asks for “long-term outcomes after 12 months”. Your Problem finding says there is no follow-up data, so I wrote a placeholder rather than an answer.
    • “Sustainability plan — 150 words” was last updated 2 May 2025, over a year ago.
    • Question 5 has a 200-word limit; the programme block is 250 words. I cut it to 198, and the cut sentences are listed at the end of that finding.
ChatGPT, connected to know.sh, assembles a first draft from the approved blocks and says where the library ran out. The foundation and its questions are invented.

Grant libraryNo. 5

Outcomes and datathe numbers funders ask for

12 findings, 2,310 words, about 10 minutes, filed 9 February, revised 14 August, 4 highlights.

Every figure used in an application comes from here. Numbers are for fiscal year 2026 (July to June), checked against the evaluation report and approved by the executive director on 14 August 2026.

If a number changes, change it here first. Other blocks refer to these findings rather than repeating them.

Findings

  1. 1Learners served, FY2026Evidence, keyLast updated 14 August 2026. 312 adult learners enrolled; 241 attended ten or more sessions.
  2. 2Reading-level gains, pre- and post-testEvidence, keyLast updated 14 August 2026. 68 per cent of learners who took both tests gained one level or more.
  3. 3Waitlist and unmet demandEvidenceLast updated 3 July 2026. 87 adults on the waitlist at year end; median wait eleven weeks.
  4. 4Cost per learner — 100 wordsEvidenceLast updated 14 August 2026. Total programme cost divided by learners with ten or more sessions.
  5. 5No follow-up after learners leaveProblemWe do not track employment or further study after exit. What we say when asked, and what it would cost to start.

and 7 more findings

The numbers document: each figure is one finding, with the date it was last checked on its first line. The organisation and its figures are invented.

Prompts to try

  1. Your assistant, connected to know.sh (Claude, ChatGPT or a local model)

    I am attaching our five funded applications from 2025–26 and the FY2026 annual report. Using know.sh, make a shelf called “Grant library” with five documents: Organisation, Programmes, Outcomes and data, Budget narratives, Letters of support. File each recurring answer as a finding in the right document, titled with its subject and word count. Where applications answer the same question differently, file both versions and name the application each came from. Take every figure exactly as written; do not update or round anything.

  2. Your assistant, connected to know.sh (Claude, ChatGPT or a local model)

    Using my shelf “Grant library” in know.sh, answer these funder questions as a new document on the shelf, one finding per question, within each word limit: [paste questions and limits]. Use only existing blocks, name the blocks you used, and flag any block last updated more than twelve months ago. Where the library has no answer, write “Needs an answer” rather than drafting one.

  3. Your assistant, connected to know.sh (Claude, ChatGPT or a local model)

    Using know.sh, read every finding on my shelf “Grant library” and list any figure (learners served, cost per learner, budget totals, staff numbers) that appears with different values in different findings, quoting both and naming the findings. Then list every block whose “Last updated” line is before 1 September 2025, oldest first. Do not change anything.

Variations

  • Keep a Funders document with one finding per foundation you apply to: its priorities, deadlines, word limits, its rules on AI use, and what the programme officer said on the last call.
  • Use the same library for the annual report: the programme and outcomes blocks are most of what it needs.
  • Pair it with the onboarding handbook so a new development assistant learns the organisation from the same approved language.

Where it falls short

  • No funder portal connects to know.sh. Answers are copied into each portal by hand.
  • Budgets are spreadsheets, and know.sh has no spreadsheet or calculation. Keep the budget in your accounting system or a spreadsheet; the library holds the narrative that explains it.
  • Signed letters, audits and IRS determination letters cannot be stored as files. Record where each one is kept.
  • Your assistant can only draft from what is in the library. If the library is stale, the draft is stale, however fluent it reads.
  • Smaller local models call tools less reliably and lose track of word limits more often. Check what they file; Revisions shows every change.

A note on accuracy and funders’ rules on AI

Indexed under

Grant applicationsBoilerplateLetters of supportOutcomes dataWord limitsNonprofitsAnnual refresh

See also