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5.2 Configuring Prompt Columns and Row Summaries

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Microsoft Certified: Intelligent Applications Builder Associate AB-410

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1 Introduction

1.1 About the AB-410 Exam 1.2 How to Use This Study Guide

2 Chapter 2: Designing Power Platform Solutions with AI-Enabled Tools

2.1 Analyzing Requirements and Identifying Components 2.2 Evaluating Built-in Agents for Business Solutions 2.3 Recommending Extensibility Options

3 Chapter 3: Environments and ALM Strategy

3.1 Recommending Environment Types 3.2 Solution and Application Lifecycle Management Strategy

4 Chapter 4: Dataverse Tables and Columns

4.1 Creating and Editing Tables in the Data Workspace 4.2 Configuring Table Properties 4.3 Creating and Modifying Columns

5 Chapter 5: Relationships, AI Data Features, and Data Security

5.1 Configuring Table Relationships 5.2 Configuring Prompt Columns and Row Summaries 5.3 Configuring Public Views and Main Forms 5.4 Configuring Dataverse Security

6 Chapter 6: Model-Driven App Forms and Views

6.1 Creating and Configuring Forms for Model-Driven Apps 6.2 Creating and Configuring Views for Model-Driven Apps 6.3 Creating Generative Pages with Natural Language

7 Chapter 7: Composing, Securing, and Visualizing Model-Driven Apps

7.1 Composing a Model-Driven App 7.2 Configuring Access to Model-Driven Apps, Forms, and Views 7.3 Model-Driven App Charts and Dashboards

8 Chapter 8: Creating and Designing Canvas Apps

8.1 Creating a Canvas App Using Data 8.2 Designing for Accessibility, Performance, Responsiveness, and Usability 8.3 Automating Business Processes from Canvas Apps

9 Chapter 9: Canvas App Components, Variables, and Error Handling

9.1 Reusable Components, Named Formulas, and User-Defined Functions 9.2 Managing Variables and Collections 9.3 Implementing Error Handling in Canvas Apps

10 Chapter 10: Testing Canvas Apps and Creating Agents from Apps

10.1 Testing Canvas Apps with Monitor 10.2 Creating a Copilot Studio Agent from a Canvas App

11 Chapter 11: Cloud Flow Triggers, Connectors, and Approvals

11.1 Recommending Cloud Flow Triggers 11.2 Evaluating and Recommending Connectors 11.3 Managing Approvals with Cloud Flows

12 Chapter 12: Cloud Flow Actions, Control, and Troubleshooting

12.1 Configuring Cloud Flow Actions 12.2 Implementing Flow Control with Conditions and Loops 12.3 Testing and Troubleshooting Cloud Flows

13 Chapter 13: Building Prompts in AI Hub

13.1 Building Prompts from Templates or from Blank 13.2 Adding Inputs to a Prompt 13.3 Adding Knowledge to a Prompt

14 Chapter 14: Prompt Settings, Models, and Consuming AI

14.1 Customizing Prompt Settings and Models 14.2 Consuming Prompts in Apps and Cloud Flows 14.3 Consuming AI Models in Apps and Cloud Flows

15 Chapter 15: Business Rules and Business Process Flows

15.1 Configuring Business Rules 15.2 Configuring Business Process Flows

16 Chapter 16: Calculated Columns and Business Logic Strategy

16.1 Calculated, Rollup, and Formula Columns 16.2 Evaluating Use Cases for Business Logic

5.2 Configuring Prompt Columns and Row Summaries

Key Takeaways Explain This Topic AI

A prompt column stores AI-generated text as persistent Dataverse data, grounded in other columns of the same row — up to five prompt columns per table.

Prompt column values regenerate only on record creation or when an input column referenced by the prompt is updated — there is no schedule, no on-demand run, and no automatic backfill of existing rows.

Every prompt column adds (name)_PromptColumnStatus and (name)_PromptColumnDetails columns, with status codes 0 NotStarted, 1 InProgress, 2 Completed, 2000/2001 skipped, and 3 Failed.

Row summaries are per-table Copilot digests applied to all main forms and views of the table; they require the AI insight cards environment setting and at least one row of data, and they are not available for Case, Lead, or Opportunity.

Prompt columns use the same licensing model as AI Builder prompts (Copilot credits); adding filter conditions reduces unnecessary executions and credit consumption.

Last updated: July 2026

Prompt Columns

A prompt column is an AI-powered data type in Microsoft Dataverse: instead of a user typing a value, the column's content is generated by a natural-language AI prompt that you ground in other columns of the same row. The generated result is stored persistently in the table , so it behaves like ordinary data — available to views, forms, workflows, and reports.

Typical uses include condensing a long customer feedback column into a two-sentence brief, classifying sentiment, extracting action items from notes, or drafting a reply suggestion from a support inquiry.

Creating a prompt column

In make.powerapps.com , open Tables , open your table, and select New → Column .

Give the column a display name, then set Data type to Prompt .

Select +Add new prompt and write the instruction. Ground it with +Add content , choosing one or more input columns from the table.

Save the prompt, then save the column.

Key limits and rules:

A table supports up to five prompt columns .

Input columns cannot be formula columns, file columns, image columns, or another prompt column — those inputs are ignored.

A default text input variable , applied as a filter on the table's primary column, is required in the prompt definition; saving fails without it.

The environment's Block unmanaged customizations setting must be off to create or edit prompt columns.

Testing before you ship

Create a dedicated test record with realistic values in every input column. While editing the prompt, select the input column to open Filter knowledge , choose Filter attribute , and point it at your test record. Select Test to review the Model response , and open the Knowledge used tab to confirm the prompt read the record you intended. Iterate on the wording, set the filter back to No filter , save — and finish by checking real output on a model-driven app form or view.

When values are generated — and when they are not

Prompt columns compute asynchronously , decoupled from the save transaction so record operations stay fast. The execution triggers are precise, and the exam probes them:

  • Trigger Result
  • New record created Prompt runs for that record
  • An input column referenced by the prompt is updated Prompt re-runs and refreshes the stored value
  • Only an unrelated column is updated No meaningful execution (logged as skipped, status 2001)
  • Prompt definition edited Nothing recalculates until an input column changes — there is no on-demand re-run

Rows that existed before the column was added Not backfilled ; updating a referenced input column forces generation

Every prompt column automatically gets two companion columns, (name)_PromptColumnStatus and (name)_PromptColumnDetails . Status codes: 0 NotStarted , 1 InProgress , 2 Completed , 2000 (skipped — filter conditions not met), 2001 (skipped — no referenced input column changed), 3 Failed (error text in the Details column).

You can narrow execution with filter conditions so the prompt runs only when a condition is true (for example, only when Status equals Resolved ) — this conserves Copilot credits . Generation can be switched off per column with Allow prompt column execution , and the feature also requires the tenant/environment-level Copilot and AI Prompts feature settings; both levels must be enabled for execution to happen.

Licensing and governance (awareness level)

Prompt columns use the same licensing model as AI Builder prompts — execution consumes AI Builder / Copilot credits, and missing entitlements or exhausted credits are a documented cause of failed generation.

Prompt columns are not audited , so do not rely on the audit log to reconstruct who generated what.

A common failure cause is a user lacking read permission on an input column (column-level security applies to the prompt's grounding data), which produces status 3.

Exam trap: candidates often assume prompt columns recalculate on a schedule or when the record is opened. They do neither — the only refresh paths are record creation and updates to the columns the prompt references.

Row Summaries

A row summary is a Copilot-generated, natural-language digest of a Dataverse row, surfaced in model-driven apps either as a collapsible summary bar at the top of a main form or per row from views . It exists to save users from scanning dozens of columns spread across tabs and related tables.

Enabling and configuring

Prerequisite (admin): a Power Platform administrator enables the AI insight cards environment setting (Power Platform admin center → environment → Settings → Features ). The setting defaults to On , and newer admin surfaces also expose it under Copilot → Settings → Power Apps → Summary .

Maker configuration: in make.powerapps.com, open the table and under Customizations select Row summary . If the option is disabled, hover over it to see why — for example, the table must contain at least one row of data .

In the Prompt box, add the columns to summarize by selecting Add or typing / . You can steer formatting (bulleted list versus paragraph) and request generation in the user's preferred language .

Select Test prompt — the preview is generated from the most recently edited row in the table.

Select Apply to main forms .

Scope rules the exam cares about:

The configuration is per table and applies to all views and main forms of that table, in every model-driven app that uses them. Forms carrying a summary show a form AI icon in the table's Data experiences → Forms list.

Tables that already ship their own app summaries — Case , Lead , and Opportunity (Dynamics 365 Customer Service and Sales) — are excluded from this feature to avoid conflicts.

Prompt columns vs row summaries

  • Prompt column Row summary
  • Output Stored in a column as data Rendered on the form or view , not stored as data
  • Scope That table's rows, record by record All main forms and views of the table
  • Configured at New column, data type Prompt Table → Customizations → Row summary

Choose a prompt column when downstream logic (flows, reports, business rules) needs the AI-generated text as data; choose a row summary when the goal is a quick, human-readable orientation at the top of a form.