Describe your program and Sopact Sense lays out the logic model — inputs through impact — grading every box by evidence and flagging the orphan activities and gaps a funder spots first.
In short: A logic model lays your program out in columns — inputs → activities → outputs → outcomes → impact — so funders can see the line from what you do to what changes. To build one fast, describe your program (or paste its page) to Sopact Sense; it fills every column and grades each box by evidence — green where data backs it, amber where it's an orphan activity or an untested claim, red where a column is missing — so the gaps a reviewer circles first are already flagged. The steps below show how, with copy-paste prompts.
A logic model is only as honest as what you feed it. Start from what your program actually states — not the version in your head — and let Sense work from that. Set up the Assistant so it sticks to your words:
You are the Sopact Sense Assistant. Here is my program description: [PASTE PROGRAM DESCRIPTION]. Use only what it states; mark anything you infer as [INFERRED]. Wait for my task.
You don't draw the five columns — you write the prompt that fills them and grades them. This is the one that builds the logic model and flags the gaps:
Build a Logic Model for [PROGRAM]: inputs → activities → outputs → short / long-term outcomes → impact. Flag gaps and orphan activities and suggest one indicator each. Use only what the program states; mark anything inferred [INFERRED]. Grade every element green / amber / red.
No written description handy? Point Sense at your program's public page instead:
Build a Logic Model from this program page: [PROGRAM URL]. Read only what the page states; mark anything inferred [INFERRED]. Map inputs → activities → outputs → short / long-term outcomes → impact, flag gaps and orphan activities, suggest one indicator each, and grade every element green / amber / red.
Five elements make it work: the input ([PROGRAM] or its page); the columns (inputs through impact); gaps and orphan activities (an activity that feeds no output, an outcome with no activity behind it); no hallucination (mark anything inferred); and the grade (green / amber / red at a glance).
Sense returns the five columns filled from your description, every box graded, and the orphan activities and missing links called out — so the first time you read the model across, you can see where the logic breaks. The demo runs on Vista Workforce Collaborative, engineered to grade one green, one amber, one red:
Vista Workforce Collaborative runs a 12-week coding bootcamp for ~60 low-income adults per cohort. Activities: 200 hours of instruction, weekly 1:1 mentoring, and an employer demo day. In the latest cohort, 41 of 58 graduates (71%) were placed in a tech job within 6 months, verified by employer letters. We believe the 1:1 mentoring builds the confidence that keeps participants engaged. We do not yet track whether participants stay employed beyond the first placement or whether wages grow over time.
GRADE: green | 71% placed | 41 of 58, verified by employer letters; amber | employer demo day | orphan activity — feeds no output; red | wage growth | not tracked beyond placement
The green column is carried by verified data, the amber flag is an orphan activity — the demo day that leads nowhere on the model — and the red box is the missing long-term outcome: no wage or retention tracking at all.
The model is worth most when you fix what it exposes. Take the lowest-graded element and make it measurable with one realistic change:
Take the lowest-graded element above and fix it using only what the program could realistically measure. Show the before → after grade and the single indicator/edit that moves it to green.
For Vista, that's a 12-month employment-and-wage check on past graduates — turning the red long-term box into something the program can actually report.
Generate a decision-first report in your own brand, then a shareable link:
Create a 'missing & incomplete' report from this analysis in Sopact branding [or paste your website URL / brand guideline to apply your own]. List every element graded amber or red, what is missing, and the one input that fixes each. Lead with the decision this report informs.
Create a shareable link for this report and open it in a new tab.
Hunt the orphan activities. The fastest way to spot a weak logic model is to find an activity that feeds no output — like a demo day that isn't connected to a placement or a portfolio. Ask Sense to list orphan activities explicitly; funders notice them first.
Logic model vs theory of change. A logic model is the columns — what goes in, what comes out. A theory of change adds the assumption under each arrow. If you've already built one, ask Sense to convert it: "Turn this logic model into a theory of change and name the assumption under each link."
Turn one box green per cycle. Add the one indicator Sense suggests for your reddest box, collect it next cohort, and re-run — watch the model fill in over time.
Tighten your program page while you're here. Once Sense has graded the model, ask it to fix your program page so the claims match the evidence:
Based on the grades above, suggest edits to my program page so its claims match the evidence. Flag every sentence that overstates what we can show, and rewrite it to be accurate and specific.
A logic model is a one-page map of a program in five columns — inputs → activities → outputs → outcomes → impact — that shows the line from the resources you put in to the change you expect out. It's the framework most funders ask for because it makes a program's logic checkable at a glance.
A logic model shows the columns — what goes in and what comes out, in sequence. A theory of change goes a step further and names the assumption under each arrow: why you believe one box leads to the next. The logic model is the structure; the theory of change explains why it should work. Many teams build the logic model first, then add the assumptions to turn it into a theory of change.
Describe your program in a few sentences (or paste its web page) and ask the AI to fill the five columns, flag gaps and orphan activities, suggest one indicator per element, and grade each box green, amber or red by how much evidence supports it. In Sopact Sense this takes minutes and stays grounded only in what your program states — it marks anything inferred so it never invents outputs you don't have.
Open Sopact Sense, paste your program description, and put it to work.
Try in Sopact
