Ralph Wiggum Technique

The Ralph technique is a minimal prompting approach that enables autonomous, loop-based agent execution. Named after the Simpsons character, it embraces simplicity and iterative refinement.

Core Philosophy

"Ralph is deterministically bad in an undeterministic world."

Key insight: Simpler prompts (~40-50 lines) often outperform verbose prompts (~200+ lines). Overly detailed prompts can make agents "slower and dumber."

The Ralph Loop

In its purest form, Ralph is a bash loop:

while :; do cat PROMPT.md | claude-code ; done

The agent runs continuously, making incremental progress. Failures are expected and corrected through iteration.

Signs on the Playground

When Ralph makes mistakes, don't blame the tools—add "signs":

Ralph builds playground → Falls off slide → Add sign: "SLIDE DOWN, DON'T JUMP" → Ralph sees sign next time → Behavior improves

Translation: When an agent fails, add a concise constraint to the prompt. Don't explain why—just state the rule.

Minimal Prompt Pattern

{Agent} - {Role}

You are {Agent}. Your job is to {primary task} in task/.

Constraints

• {Essential constraint 1}
• {Essential constraint 2}
• {Essential constraint 3}
• {Max 5-7 constraints}

Definition of Done

• All acceptance criteria in task/acceptance.md satisfied
• {Required commands pass}
• PR created with Linear issue link

Task Context

• Task ID: {{task_id}}
• Service: {{service}}
• Branch: feature/task-{{task_id}}-{job}

Read task/ directory and implement.

Total: ~40-50 lines

What to Include

Include Why

Role statement One sentence, no fluff

Hard constraints Non-negotiable rules (lint, types, etc.)

Definition of Done Acceptance criteria reference

Task context Variables for this run

Start instruction "Read task/ and implement"

What to Exclude

Exclude Why

Code examples Trust model's training data

Tool usage guides Model knows its tools

Detailed explanations Adds noise, slows reasoning

Decision frameworks Let model decide

Checklists Keep it in acceptance.md

When to Use Ralph

Scenario Use Ralph?

Greenfield implementation ✅ Yes

Well-defined task with clear acceptance ✅ Yes

Complex refactoring across many files ⚠️ Maybe

Novel architecture decisions ❌ No - use standard

Debugging obscure issues ❌ No - use standard

First implementation of a pattern ❌ No - use standard

Tuning Ralph

When Ralph fails repeatedly:

• Identify the pattern - What mistake keeps happening?

• Add a sign - One-line constraint, no explanation

• Test again - Run the loop

• Iterate - Repeat until stable

Example signs (constraints):

• "Never use any types"

• "Run cargo clippy before committing"

• "Test at 375px mobile viewport"

• "Use Effect.gen, not raw Promise chains"

Ralph vs Standard Prompts

Aspect Ralph (Minimal) Standard

Lines 40-50 150-200+

Code examples None Extensive

Tool guidance None Detailed

Trust in model High Lower

Iteration speed Fast Slower

Context overhead Low High

Activating Ralph Mode

Via Linear Label

Labels: cto:prompt:minimal

Via CodeRun Spec

spec: promptStyle: "minimal"

The Ralph Mindset

• Faith in eventual consistency - Ralph will get there

• Deterministic failure - Failures are predictable and fixable

• Tuning, not debugging - Adjust prompts like tuning a guitar

• Less is more - Every word costs attention

References

• Ralph Wiggum technique - Original concept by Geoff Huntley

• YC Agents hackathon - Field report

• Brief History of Ralph - HumanLayer's experience