Giving AI Roles and Names

Source: Dev.to

The Instability Problem

Ask the same question to AI twice. You’ll get different answers.
Not wrong answers—just inconsistent. Different emphasis, different structure, different depth. The AI has no anchor, so it drifts.

This isn’t a flaw. It’s a feature of general‑purpose systems. AI tries to be helpful in whatever direction seems relevant. Without constraints, “relevant” varies with every interaction.

Unpredictability is the cost of versatility.

The Stabilizing Effect of Purpose

Give AI a defined role, and outputs stabilize.

The role constrains the possibility space. AI stops trying to be everything and starts being something specific.

This isn’t limiting AI—it’s focusing AI.

No Label vs. Labeled: The Difference

The label doesn’t add knowledge. It adds direction.

AI Is Not Omnipotent

Here’s the uncomfortable truth: a single AI role cannot handle all problems.

• Implementation requires different thinking than review.
• Testing requires different focus than design.
• Documentation requires different skills than debugging.

A “general assistant” can attempt all of these. A specialist excels at one.

Versatility and excellence trade off. Choose excellence for each domain.

The Multi‑Role Solution

Instead of one general AI, create specialized roles:

Each role has:

• A defined scope (what it handles)
• A defined perspective (how it approaches problems)
• Consistent behavior (predictable outputs)

You’re not creating multiple AIs. You’re creating multiple lenses through which AI operates.

When Roles Fall Short

Sometimes a problem doesn’t fit existing roles. Two options:

Option 1: Create a New Role

When the gap is categorical—a type of work not covered.

Example: You have implementation and testing roles, but need someone to manage progress and coordinate. Create a project management role.

Option 2: Deepen an Existing Role

When the gap is specificity—the role exists but lacks detail.

Example: Your “reviewer” role catches issues but misses your specific architectural patterns. Add architectural knowledge to the role definition.

The Name Effect

Names aren’t just labels. They’re identity anchors.

When you name a role “Naruse” instead of “Implementation AI,” something shifts:

• You refer to the role consistently.
• The role develops recognizable patterns.
• Collaboration feels less transactional.
• The “team” becomes tangible.

This is psychological, not technical. But psychology affects how you work. A named teammate gets context you wouldn’t bother explaining to a tool.

Role Definition Structure

A useful role definition includes:

## [Name]: [Title]

### Scope
What this role handles. What it explicitly does NOT handle.

### Perspective
How this role approaches problems. What it prioritizes.

### Standards
Quality criteria this role applies.

### Boundaries
When to escalate. When to defer to other roles.

The more specific the definition, the more stable the output.

The Stability Payoff

With defined roles:

Roles don’t limit AI. They unlock consistent excellence.

This is part of the “Beyond Prompt Engineering” series, exploring how structural and cultural approaches outperform prompt optimization in AI‑assisted development.