Create advanced Works with: Any AI

Logic Model Builder

Table output Has example

Connect activities to measurable outcomes.

The Open buttons send the prompt text as currently shown below to that site only when you click. Fill in the placeholders first.

[VERIFY] is an editorial flag, not a blank to fill. Wherever the prompt shows [VERIFY], check that item against a reliable source before you use it.

Build a logic model for [PROGRAM_NAME] at [ORGANIZATION]. Population served: [POPULATION]. Resources: [RESOURCES]. Planned activities: [ACTIVITIES]. Timeline: [TIMELINE]. Funder's language for outcomes: [FUNDER_LANGUAGE].

Return a table with columns: Inputs, Activities, Outputs (with numbers), Short-term outcomes (0-6 months), Medium-term outcomes (6-18 months), Long-term impact. Make each outcome measurable and specify who changes and how.

Then:
1. For each outcome, give one indicator, a data source, a collection method, and frequency.
2. List assumptions and external factors that could affect results.
3. Mark any outcome that is too big for this grant's budget and timeline.
4. Provide a one-paragraph narrative version of the logic model.
5. Check for common mistakes: outputs confused with outcomes, activities with no outcome, outcomes with no activity.

Use only the activities and numbers I gave; mark anything inferred as [VERIFY]. Ask which outcome the funder cares about most.

Use plain words that a community member could understand, and keep each cell short enough to fit on a one-page chart that I can paste straight into an application.

Verify every fact, figure and claim in the output against your own records or the official documentation, and have a qualified professional check anything with legal, safety or financial consequences before you rely on or publish it.

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Optional: type your details and the prompt above updates. Nothing is saved or sent.

Replace each bracketed placeholder with your own details, then paste into Any AI or another assistant.

Inputs | Activities | Outputs | Short-term outcomes (0-6 mo) | Medium-term (6-18 mo) | Impact
2 staff, 20 volunteers, library space | 12-week tutoring, 2 sessions a week | 60 students, 1,440 tutoring sessions | Students gain confidence reading aloud | 70% improve one reading level | District reading proficiency improves
Indicators: sessions attended (attendance records, weekly); reading level (assessment, start and end); confidence survey (5 questions, start and end).
Assumptions: schools share assessment data; volunteers stay for the term.
External factors: school closures.
Outcome too large for this grant: district proficiency (long-term impact only).
Narrative: "Volunteer tutors give 60 students twice-weekly reading support for 12 weeks..."
Mistake check: outputs and outcomes separated.

• logic-model