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How to Use MUSE

One of MUSE's most powerful features is its ability to generate a complete, evidence-linked logic model from a plain-language description of your program. Instead of building each node by hand, you simply describe what your intervention does — and the AI handles the rest.

How It Works​

When you click "Generate Logic Model," MUSE's AI:

  1. Reads your description and identifies the core intervention
  2. Constructs a full logic model with nodes at every stage (Activities through Impact)
  3. Searches the evidence database for peer-reviewed research relevant to each causal connection
  4. Places everything on the canvas, complete with green evidence-backed arrows where research was found

The whole process takes about 40 seconds.

Step-by-Step Instructions​

Step 1: Open the Canvas​

Go to muse.beaconlabs.io/canvas or click "Canvas" in the top navigation bar.

Starting Fresh

If you already have nodes on the canvas, the AI will add to what's there. If you want a clean slate, go to More → Clear All before generating.

Step 2: Click "Generate Logic Model"​

In the toolbar at the top of the canvas, click the "Generate Logic Model" button (the one with the robot icon). A dialog box will appear.

Step 3: Describe Your Intervention​

In the text box, type a description of your program or intervention. You have up to 1,000 characters to work with.

Your description should answer: What does your program do, for whom, and what change are you hoping to create?

Good examples to try:

"Impact of open source software contributions on the Ethereum ecosystem"

"Reducing food insecurity in urban communities through community gardens"

"Improving maternal health outcomes through mobile health interventions in low-income countries"

"Providing coding bootcamps to unemployed adults in rural areas to improve employment outcomes"

Below the text box, you may see a toggle labeled "Search external academic papers." When enabled, MUSE will also search Semantic Scholar for additional academic papers related to your logic model's causal connections.

About External Papers

External papers are shown as reference material only — they are not scored by MUSE's AI and do not receive blockchain attestations. They supplement the curated evidence database with broader academic literature, including TLDR summaries and citation counts to help you quickly assess relevance.

Step 5: Click "Generate"​

Once you're happy with your description, click the "Generate" button. The dialog will show a live progress tracker.

Step 6: Watch the Progress​

The AI works through the following steps, and you'll see each one tick off as it completes:

StepWhat's Happening
Step 1: Analyzing goalThe AI reads your description and understands the core intervention
Step 2: Generating logic modelThe AI constructs the full set of nodes and causal connections
Step 3: Searching for supporting evidenceThe AI searches the evidence database and links relevant research to your causal arrows
Step 4: Searching external academic papers(Only when enabled) The AI searches Semantic Scholar for additional papers
Step 5: Completed!Your logic model is ready

Step 7: Review Your Logic Model​

Once generation is complete, the dialog closes and your logic model appears on the canvas. You'll see:

  • Color-coded nodes for each stage: Activities (orange), Outputs (green), Short-term Outcomes (blue), Intermediate Outcomes (yellow), and Impact (purple)
  • Causal arrows connecting the stages in a left-to-right flow
  • Green arrows where the AI found supporting research evidence
About Green Arrows

Green arrows are special — they indicate that a peer-reviewed research study supports that causal link. You can click on a green arrow to see what evidence is attached.

After Generation: Refining Your Model​

The AI-generated logic model is a starting point, not a finished product. You are expected to review and refine it:

  • Edit any node — Double-click a node or click the pencil icon to update its title and description
  • Add metrics — Open any node's edit panel to attach measurable indicators
  • Remove connections — Click an arrow and press Delete to remove it
  • Add new connections — Drag from one node's handle to another to create a new causal arrow
  • Add new nodes — Use the Add Logic button to create additional nodes that the AI may have missed
  • Rearrange the layout — Drag nodes to positions that make more visual sense for your model
Review for Accuracy

AI-generated content reflects patterns in the evidence database and may not perfectly match your specific program context. Always review the generated nodes and evidence links with your own expertise before sharing or publishing.

What If Generation Fails?​

If the generation encounters an issue, MUSE will display a specific error message:

Error TypeWhat It MeansWhat To Do
High demandThe AI model is currently busyWait a moment and try again
Rate limitToo many requests in a short timeWait a minute before retrying
TimeoutThe operation took too longTry a simpler description, or try again later
Invalid inputIssue with the descriptionModify your description and try again

If the error persists:

  1. Close the dialog
  2. Check your internet connection
  3. Try the dev version to see if the issue is isolated to the production environment

Next Steps​