Intro:
In model-driven apps, business rules often require dynamic calculations—whether it's computing discounts, deriving age from birthdate, or classifying customer feedback. While client-side scripting using custom web resources can achieve these outcomes, it introduces latency, complexity, and maintenance overhead.
For rule-based logic that depends on structured data, server-side calculations offer a more efficient, scalable, and secure alternative. With Dataverse's native capabilities like Calculated Columns and Prompt Columns, makers can offload logic to the backend—ensuring consistent performance, centralized governance, and seamless integration with Power Platform components.
Dataverse Intelligence: Calculated Columns vs. Prompt Columns:
As Dataverse evolves to support both deterministic logic and generative intelligence, choosing the right column type becomes essential. This post compares Calculated Columns and Prompt Columns, helping makers and architects decide when to use each.
Calculated Columns:
Calculated Columns are formula driven and Automatically compute values based on expressions and other columns.
A simple illustration would be as below where a new column ( date field) is set to a value by configuring a simple adddays function
Prompt Columns:
Prompt Columns are pre-view feature that could assist with AI-assisted generation of text, summaries, classifications, or suggestions based on some value for a record.
In this illustration , a new column (text format) would be classifying an order based on the total value of the order.
| Feature / Criteria | Calculated Columns | Prompt Columns |
|---|---|---|
| Purpose | Automatically compute values based on expressions and other columns. | Use AI-generated values based on user-defined prompts and context. |
| Use Case | Business logic, formulas, conditional values, date math, etc. | AI-assisted generation of text, summaries, classifications, or suggestions. |
| Technology Base | Power Fx expressions (similar to Excel formulas). | Generative AI (LLMs) integrated via Copilot Studio. |
| Triggering Mechanism | Triggered on data change or record creation. | Triggered manually or automatically based on prompt configuration. |
| Data Type Support | Supports numeric, text, date, boolean, and lookup types. | Primarily text-based outputs (e.g., summaries, suggestions). |
| Customization Level | High control via expressions and conditional logic. | High flexibility via prompt engineering and AI model tuning. |
| Performance | Fast and deterministic; computed at runtime or save. | May involve latency due to AI processing; non-deterministic. |
| Security & Compliance | Fully within Dataverse; no external calls. | May involve external AI services; requires governance and data sensitivity review. |
| Versioning & Debugging | Easy to debug with formula editor. | Requires prompt testing and evaluation frameworks. |
| Example Scenario | Calculate age from birthdate, apply discount logic. | Generate a product description from features, classify sentiment from comments. |
| Limitations | Limited to deterministic logic; no AI or NLP capabilities. | Requires prompt tuning; may produce inconsistent results. |
| Documentation Reference | Calculated Columns | Prompt Columns |
Closing Remarks:
Calculated Columns are retroactive—they apply logic to both new and historical records, ensuring consistency across the dataset. In contrast, Prompt Columns are forward-looking; they generate values only at the time of record creation or update, based on the configured prompt. This makes Calculated Columns ideal for rule-based logic that must be uniformly applied, while Prompt Columns are better suited for contextual enrichment during data entry.
Calculated Columns are your go-to for precision and speed. Prompt Columns unlock creativity and contextual intelligence. Together, they form a powerful duo for building smart, responsive apps in Dataverse.
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