Sample data

Sample data lets your team inspect an email before choosing to send it. Use a representative customer and the fields the template expects, then check the rendered subject, content and links. A sample is review material; real recipient data is resolved for the actual send.

Preview two customer situations

1. Open Data, then choose a customer under Sample.
2. Open Preview. Inspect Base for fixed content and Liquid; use Generate preview when Adaptyle should write the personalized version.
3. Switch to a customer with less data, or use Edit as JSON to remove an optional value from the sample. Regenerate personalized content and check the fallback.
4. Use Send test after the rendered result is ready to inspect it in an inbox.

For an announcement, the two useful outcomes might be a greeting with the customer’s name and a general greeting when the name is missing. Editing this sample does not edit the customer record.

What an email needs

Every email template lists the information it needs, and it can only reference what is on that list. A welcome email needs a contact. A receipt might need a contact, an order, and a list of items. The template reference calls this list expectedDataRepresentation; a send supplies matching keys under data.

Each entry is either a record or a plain JSON value.

Your sample is a JSON object with one key per entry. Each value has to fit: required fields present, the right types, and any constraint honored (a set list of allowed values, an email format). Where a value does not fit, that part of the preview cannot render.

Where you edit it

Open the editor’s Data tab. The upper section lists the email’s expected properties and To recipient (called recipientDataPath in the reference). The Sample section below it holds the record pickers.

Choose Edit as JSON to open the raw sampleDataStringified value in a JSON5 editor, with record pickers and AI generation controls. Choose Hide JSON to return to the compact sample view. Editing the JSON changes the preview, not the selected customer record.

Using a real record

In both views, each record entry has a picker listing records from its schema. Pick one and its fields are written into the sample under that entry's key. Contact and object entries you have not chosen yet fill themselves with the first available record, so the preview starts populated. Event entries are left for you to choose. Opening Edit as JSON fills anything still empty, events included, with a record picked at random from the first ten.

Edit as JSON also has a shuffle control that refills every record entry at once. In the sample pickers, an entry whose schema holds no records shows a Generate action instead of a picker.

Generating with AI

The editor can write the sample for you. In Edit as JSON, open Generate sample data by AI. It reads the list of what the email needs and your schemas, then invents believable values: names, dates, emails, all within the limits each field allows. maxclicks checks the result against that list before accepting it.

The dialog has an optional prompt field. Leave it blank for ordinary data, or describe the scenario you want:

Generate data for a user who just signed up, include a welcome offer code.

The Generate action beside a single entry (shown when its schema holds no records) fills that one entry, with no prompt.

Build a useful review set

Use real records to test your data mapping and invented samples to test edge cases. An AI-generated record is not evidence that a matching contact or order exists, and editing the sample does not update your customer records.

For a win-back email, review a frequent buyer, an occasional buyer and a contact with no usable purchase history. Include missing first names, empty related-record lists and null subject results from a valid calculation. Give Liquid a fallback and give Adaptyle instructions for that missing context. Test unavailable or expired scheduled evaluations separately: those are execution-readiness failures, not ordinary empty customer fields that a greeting fallback can repair.

Generate the personalized preview again after changing the sample. Send test sends the rendered sample to your chosen test address; it does not run the production recipient lookup or prove that the recipient has marketing consent. See the editor’s test-send behavior.

Keeping the sample in sync

What the email needs, and who it is addressed to, can drift away from where it is being sent. A broadcast fixes both to the contact in its audience, so the editor shows:

• Data out of sync: a Sync data properties action that resets the list to match the audience.
• Sync recipient: appears when the email is addressed to something other than the audience contact.

A standalone template sets its own list and its own recipient, so neither is forced.

Why a later refresh may not change an active send

A standalone sample resolves selected scheduled Evaluated fields against its current read context. A broadcast revision or root workflow run captures the calculation generation, freshness policy and observation time on its first read; child runs inherit that context. Publishing a newer calculation does not replace that captured selection. This fixes the scheduled calculation context, not every live customer field or on-demand lookup.

An enabled calculation with no complete publication or an expired publication fails the expansion for that decision. Refreshing the calculation later does not repair an already captured unavailable selection. A paused definition can use its captured last-published policy; a valid publication with no subject result can return null. An erased pinned generation fails rather than switching to a newer one. Check the record’s Published value, Data through and status, then compare the sample with the execution context. See attribute expansion.

Related

• Liquid: reference record fields in content.
• Adaptyle: AI personalization on top of the template.
• Schemas and attributes: where the records in a sample come from.