majiayu000 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
anytypes" - "Run
cargo clippybefore 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