Anuj Sadani / Field Guide

Requirements & behavior · Standard notation

DMN & decision tables

Represent how input conditions produce an explicit decision outcome.

Intermediate · 3 drills · Reviewed 2026-10-24

See an examplePractise

What it helps you do

Expose gaps, overlaps, and priorities in a repeatable decision.

Starting knowledge: Be able to name the decision inputs, outcomes, and the owner of the policy.

  • Define input meanings and possible output values.
  • Write conditions and outcomes explicitly.
  • Declare what happens when several rows match, using the appropriate hit policy.

The problem it came from

OMG maintains Decision Model and Notation for business decisions and rules. Its design complements process notations such as BPMN while allowing decision models to stand alone.

Decision tables are a central representation, but a full DMN model also addresses decision dependencies and expressions. A simple Markdown table is not automatically a conformant DMN artifact.

Work through an example

STARTING POINT
Confirm when there is room. Return the old confirmation for duplicates. Ask for email if missing.
APPLYING THE APPROACH
First matching row wins in this teaching table:
1. Existing confirmation → return existing; allocate 0.
2. New, email missing → request email; allocate 0.
3. New, email present, place available → confirm; allocate 1.
4. New, email present, full → waitlist; allocate 0.
  • The duplicate row precedes the new-registration rules.
  • The email condition is not silently assumed.
  • First-match behavior is stated; another policy could reveal an overlap rather than select the first row.

Where it helps. Where it stops.

Use it when

  • Eligibility, routing, classification, or other bounded business rules.
  • Several prose exceptions interact and reviewers cannot see which outcome wins.

Potential benefits

  • Rows make combinations easier to compare.
  • An explicit overlap policy exposes accidental precedence.

Limits and common mistakes

  • A complete-looking table can consistently execute the wrong policy.
  • The table must address missing input and conflicting rows; execution does not create agreement.
Applying it with AI

The prompt below asks for this structure. It does not guarantee accuracy, complete coverage, better learning, or conformance to a standard. Treat those outcomes as separate checks.

Practise the judgment

Try a choice, inspect a boundary, then explain a case yourself. Hints and answers are available whenever you need them.

DRILL 01 Not attempted

Two matching rows return different outcomes and no hit policy is stated. What should you do?

Choose one answer
Hint

Row order only has meaning when the policy gives it meaning.

Explained answer

Best answer for this case: Report the ambiguity and obtain the intended policy

The table has not defined how to handle the overlap. Choosing silently invents policy.

Link to this drill
DRILL 02 Not attempted

What does an executed decision table establish by itself?

Choose one answer
Hint

Separate model behavior from external truth.

Explained answer

Best answer for this case: That the supplied model produced an outcome under its semantics

Execution is evidence about the model and inputs, not universal proof of policy or implementation correctness.

Link to this drill
DRILL 03 Not attempted

Why must duplicate handling be explicit in a capacity decision?

Hint

Which two requests can look similar but have different effects?

Explained answer & self-review rubric

A repeated submission should not accidentally allocate a second place. Its outcome differs from a valid new registration, so the table needs a distinct condition or a separate preceding decision.

  • Identify repeated versus new input
  • Preserve capacity effects
  • Make precedence or separation explicit
Link to this drill

Take it into your own work

Manual template

Use this structure yourself, with a colleague, or in your preferred writing tool.

# Decision table
Decision name:
Input definitions and allowed values:
Output definitions:
Rows: conditions | outcome
Hit policy / overlap behavior:
Missing-input behavior:
Uncovered cases:
Policy owner and confirmation:
Download template (.md)

Review the result

  • Can more than one row match?
  • Is overlap behavior explicit?
  • Are unknown inputs and missing cases visible?
Use this approach with your own AI tool
Help me apply DMN & decision tables to the material below.
Reader and existing knowledge: [describe]
Task and desired outcome: [describe]
Use these principles: Define input meanings and possible output values.; Write conditions and outcomes explicitly.; Declare what happens when several rows match, using the appropriate hit policy..
Preserve source facts, identifiers, quantities, conditions, exceptions, and uncertainty.
Separate supplied facts from assumptions. Ask for missing information instead of inventing it.
Explain your representation choices and provide a review checklist. Do not claim formal compliance or correctness from formatting alone.
Material: [paste only information you are permitted to share]
Download prompt

Go to the source

History and the underlying approach are grounded in these references. Examples, prompts, and practice cases here are original educational adaptations. Edition-specific details should be checked against the referenced publication.

Last editorial review: 2026-10-24. An overview is not certification, and this guide is not a reproduction of a standard.