To build a logic model, define the program and decision, list the resources and activities, identify their direct outputs, then connect them to intended outcomes. Check the assumptions behind those connections and record how delivery and outcomes will be assessed. A model can be drafted in a document, spreadsheet or diagramming tool. AI can help organize the material, but the responsible team must verify the content and distinguish missing information from evidence of a program problem.

This chapter is for program managers, evaluators and partners turning an agreed theory of change into a practical program structure. Bring the pathway and evidence register from the related lesson. Leave with a logic-model table, a measurement worksheet and a prioritized review list. You do not need software to learn the method.

Build the model in six steps

1. Name the scope and decision. Specify one program, population, period and intended use.
2. Separate the components. List resources, activities, outputs and outcomes without confusing what is done with what changes.
3. Connect the pathway. Explain which activities could contribute to each outcome and what assumptions must hold.
4. Map evidence and measures. Record sources, definitions, populations, timing and missing information.
5. Review gaps with the team. Distinguish a documentation gap, a measurement need and an unresolved design question.
6. Choose an action and maintain the model. Assign owners, dates and version history, then revisit the model as evidence develops.

What belongs in a logic model?

A logic model makes a program’s proposed relationships visible. CDC’s program-description guidance uses a program roadmap to connect activities with intended outcomes. Formats vary: some show outputs separately, while others describe them in the accompanying measurement narrative. Use the format required by your audience without treating one column arrangement as universal.

Do not write “run six workshops” as an outcome. It describes delivery. Similarly, a count of people enrolled does not establish improved skills. Outcomes can also concern organizations, systems or environmental conditions; they are not limited to changes in individual people. The model should reflect the actual program.

Step 1: name the decision and the boundary

Write one sentence before filling boxes: “The program team will use this model to decide which delivery and follow-up changes to test in the next cohort.” A grant reviewer may have a different decision, such as assessing whether proposed activities and resources fit the intended outcomes. Ask the reader or inspect the agreement rather than letting AI invent their priorities.

A logic model is not automatically required for every grant. Check the specific application or reporting instructions. If the funder asks for a results framework or another format, map the relevant content carefully; those formats may include different fields and definitions.

Define where this model starts and ends. A workforce program’s delivery is not the entire local labor market. Include external conditions that matter, such as employer demand, recruitment practices, transport and participant circumstances. Avoid claiming control over conditions the program cannot determine.

Step 2: turn the course pathway into a working model

Fictional course exercise. Continue the training program from the related lesson. Its records contain 80 starters, 60 known employment statuses at 90 days after exit, and 36 people employed at that checkpoint. Twenty statuses are unknown. These observations do not establish skill improvement or sustained employment, so those outcomes remain proposed until appropriate evidence is available.

Notice the last row: collecting follow-up is a measurement activity. It enables the team to assess outcomes; it does not itself cause sustained employment. Keep evaluation activities visible without confusing them with the intervention’s effects.

The table can become a diagram, but draw only the connections the team can explain. An activity may contribute to several outcomes, and several activities may support one outcome. Avoid adding arrows just to make every box look connected.

Step 3: use AI to draft, not certify, the model

The prompt below produces four working drafts: a model, an evidence table, a review of unclear connections and an improvement plan. Each draft should show its sources and the questions your team needs to resolve.

Build a draft logic model from the APPROVED MATERIAL below.
INPUTS: [program scope, intended decision, theory of change, source records,
participant input and relevant research]
1. Restate the scope and decision. Ask if either is missing; do not invent them.
2. Create: resources | activities | outputs | early outcomes | later outcomes.
   Keep observed results separate from intended outcomes. Identify assumptions.
3. For each entry, return source ID/date, supporting passage or value, relevant
   population/period, missing information, and proposed measurement question.
4. Flag unexplained connections. Say whether the available material is insufficient
   before suggesting that an activity lacks a purpose or is not measured.
5. Propose up to five review actions: issue | why it affects the decision | owner
   to consult | evidence or design discussion needed. Label proposals clearly.
Rules: use only material supplied or actually readable; report inaccessible URLs.
Do not invent counts, verification or participant experiences. A quote or number
is not proof of causal effectiveness. Do not call the output validated or grant-ready.
Return a plain table and an accessible text description of any suggested diagram.

Run one row manually first. Compare the output with the source, then inspect the full draft for omitted activities, duplicated outcomes and unsupported wording. If the source is a public webpage, label the review “public information only.” A blank page section may mean the organization has not published a record, not that the record does not exist.

AI-generated suggestions can be useful questions. They should not become facts merely because they fit the diagram. Save the draft, source version and reviewer’s changes so the next cycle starts from approved work.

Step 4: build the evidence and indicator worksheet

For each important claim, decide what evidence would support it. You do not need a survey question for every box. Use existing delivery records when they answer the question, and collect new information only when the decision justifies it.

In the employment row, 45% of all starters are confirmed employed: 36 divided by 80. Do not describe the remaining 55% as unemployed, because it includes unknown statuses. Reporting the denominator and coverage makes the result usable without hiding its limits.

Record an owner and collection moment next to each proposed measure. “Assess skills” is incomplete until someone knows which assessment, which population, when it is administered and how results will be reviewed. The later metric-definition lesson develops this worksheet further.

Step 5: diagnose gaps without guessing their cause

An unexplained connection is a question to investigate. It is not automatically proof of ineffective program design. Separate the three situations below before deciding whether to change the program or collect more data.

For example, if the public program page does not state coaching hours, do not conclude that coaching is unrecorded or that it leads nowhere. Ask what records exist and why frequency matters to the proposed pathway. Session counts might help describe delivery; they would not, by themselves, establish coaching effectiveness.

Prioritize issues by their effect on the decision. A missing outcome definition may matter more than a missing count of an incidental activity. A serious concern raised by participants may require attention even when it does not fit neatly into the current boxes.

Step 6: choose a practical improvement and keep the version

Write a short action list that distinguishes an immediate correction from a learning task. Correct an unsupported report sentence now; allow appropriate time to obtain evidence or evaluate a program change. Adding a single question should not be presented as sufficient to make an entire causal pathway “green.”

Give the model a version, owner and review date. Record changes to activities, definitions or assumptions. Keep earlier versions alongside reports that used them. CDC’s 2024 evaluation framework describes revising models as context, resources, research or evaluation findings change. The purpose is learning, not preserving an attractive diagram after the program has changed.

Watch the related framework-to-reporting demonstration

Watch the 11-minute 58-second related reporting walkthrough. It shows how frameworks and evidence can connect in a reporting workflow; it is not a validation of this fictional logic model. Browse more videos in the video library.

When software becomes useful

A document or spreadsheet can support the first model. A connected system becomes useful when several programs or partners maintain definitions, recurring submissions, documents and follow-up over time. Look for version history, source links, permissions, clear exports and a way to distinguish missing evidence from poor performance.

Sopact can support collection and source-linked analysis around these continuing records. People remain responsible for the model’s reasoning and the decision it informs. For the broader concept and software context, see the logic model guide; for delivery across cohorts, explore training and program data workflows.

Review the connections before adding more measures

Use this exercise when an activity or outcome appears disconnected. Trace one outcome backward to the activities intended to support it, then trace each activity forward to a stated purpose. A missing arrow is a question for the team, not evidence that the activity has no value.

Fictional example: The model lists training, coaching and employer introductions, but the employment outcome connects only to training. Ask whether the introductions are part of the proposed pathway and what conditions they depend on. The correction might be an explained connection, a clearer outcome or a decision to retain the activity for a different purpose. Do not add an arrow merely to pass the check.

1. Select one result important to the next decision.
2. Identify its proposed activities, direct outputs and assumptions.
3. Check whether the model explains those relationships and whether source material supports the description.
4. Separate missing documentation from a confirmed measurement gap or a design question.
5. Assign an owner and a next step. Record which version was reviewed.

Your output: one review row containing the connection, question, source, decision and next action. For example: “Employer introductions → suitable employment: clarify opportunity availability and recruitment access with the program team before adding a placement-effect claim.” A session count alone cannot resolve that causal question.

Frequently asked questions

How many components should a logic model have?

Many models show inputs, activities, outputs and outcomes, with outcomes divided by time horizon. Some include a separate impact column. Use the structure that fits the program and reporting requirements, and explain each component rather than forcing every program into identical boxes.

Is a logic model the same as a theory of change?

They overlap. A logic model organizes the program’s proposed relationships; a theory of change usually develops the explanation and assumptions behind them in more depth. A logic model can include assumptions too. Use the two as complementary working documents rather than treating the difference as an absolute rule.

Can AI create a logic model from a website?

It can draft one from information it can actually read. A public webpage is often incomplete. Identify inaccessible material and missing context, then ask the organization to verify the draft. Do not infer that an activity is unmeasured or ineffective simply because the website does not discuss it.

Does every activity need a separate survey measure?

No. Some delivery questions can be answered from routine records, and not every detail warrants measurement. Select measures based on the decision, program logic and burden. Keep important outcomes visible rather than collecting only easy activity counts.

Do funders always require a logic model?

No universal requirement applies. Read the funder’s current instructions and agreement. If a model is requested, use it to explain the pathway and assumptions clearly. An AI-generated diagram does not guarantee that an application satisfies the funder’s requirements.

What should a missing output mean?

First establish whether the output is absent from the documentation, absent from measurement, or genuinely unclear in the design. Those situations need different responses. Ask for existing records and the activity’s purpose before recommending removal or new collection.

Can a logic model prove that a program works?

No. It states a proposed explanation and helps plan evaluation. Evidence is needed to assess delivery, outcomes and causal claims. A model can be clear and internally consistent while important assumptions remain untested.

What should I do after building it?

Have staff and affected people review the model, resolve the highest-priority definition or evidence gap, and assign the next collection and review action. Keep the approved version with the evidence register so later reports use the same definitions.

Practice and continue

Turn your previous Theory of Change into a logic-model table. Ask another reviewer to classify five entries without seeing your column labels. Discuss disagreements, then trace one outcome to its proposed activities, assumption and evidence. Choose one improvement that matters to the next decision.

Next, use the Five Dimensions of Impact to examine whose outcomes are represented, how much is known and what remains uncertain. For report presentation, use How to Write an Impact Report and report examples.

By Sopact Academy · Revised September 12, 2026. The training-program example is fictional. CDC references support the method, not a claim that the example or Sopact has been certified.