Product Requirements Document: Ralph Wiggum Autonomous Coding

Document Information

Version: 1.0
Last Updated: January 2026
Owner: Development Team
Status: Active

Executive Summary

Ralph Wiggum represents a paradigm shift in AI-assisted development, enabling autonomous, iterative coding sessions that run for hours without human intervention. This PRD defines the requirements for successfully implementing Ralph-based development workflows in production environments.

Problem Statement

Current Pain Points

Human-in-the-loop bottleneck: Developers spend 60-80% of their time reviewing AI outputs and re-prompting
Context loss: AI sessions reset after each interaction, losing accumulated knowledge
Tedious mechanical tasks: Migrations, refactoring, and testing consume valuable developer time
Inconsistent quality: One-shot AI attempts produce unreliable results

User Impact

Lost productivity on repetitive tasks
Developer burnout from code babysitting
Slow delivery of mechanical improvements
High opportunity cost of manual work

Solution Overview

Ralph Wiggum enables:

Autonomous development loops running 1-24+ hours
Self-verification and iterative improvement
Git-based persistence of progress
Automated convergence to correct solutions

Target Users

Primary Personas

1. Senior Developer (Automation Focus)

Wants: Automate tedious tasks overnight
Pain: No time for maintenance work
Success: Wake up to completed migrations/refactors

2. Technical Lead (Delivery Focus)

Wants: Accelerate team delivery
Pain: Backlog of technical debt
Success: Ship quality code faster

3. Solo Developer/Founder (Resource Focus)

Wants: 10x productivity
Pain: Limited development resources
Success: One person can maintain multiple projects

4. QA Engineer (Testing Focus)

Wants: Comprehensive test coverage
Pain: Manual test writing is slow
Success: Automated test generation

Functional Requirements

Core Capabilities

1. Task Definition System

REQ-1.1: Support markdown-based task specifications
REQ-1.2: Parse measurable success criteria (3-7 criteria per task)
REQ-1.3: Define completion promises in XML format
REQ-1.4: Set iteration limits (1-50+ iterations)
REQ-1.5: Specify verification commands

2. Autonomous Loop Execution

REQ-2.1: Execute tasks in continuous loop until completion or limit
REQ-2.2: Intercept AI exit attempts via Stop Hook
REQ-2.3: Re-inject original prompt when promise not found
REQ-2.4: Provide access to previous iteration context via git
REQ-2.5: Support manual cancellation (/cancel-ralph)

3. Verification Framework

REQ-3.1: Execute automated test suites
REQ-3.2: Run linting checks
REQ-3.3: Perform type checking (TypeScript/Flow)
REQ-3.4: Validate build success
REQ-3.5: Check completion promise presence

4. Progress Tracking

REQ-4.1: Create git commits per iteration
REQ-4.2: Log iteration count and outcomes
REQ-4.3: Track API token consumption
REQ-4.4: Record error patterns
REQ-4.5: Generate progress reports

5. Cost Management

REQ-5.1: Estimate costs before execution
REQ-5.2: Track real-time spending
REQ-5.3: Enforce spending limits
REQ-5.4: Alert on threshold breaches (80%, 90%, 100%)
REQ-5.5: Provide cost-benefit analysis

User Stories

Epic 1: Autonomous Development

As a developer, I want to run migrations overnight so that I can avoid manual tedium
As a lead, I want to automate test writing so that coverage improves without team effort
As a founder, I want to complete $50K projects for $300 so that I can bootstrap efficiently

Epic 2: Quality Assurance

As a developer, I want automated verification so that Ralph doesn't produce broken code
As a QA engineer, I want comprehensive test coverage so that bugs are caught early
As a lead, I want consistent code quality so that reviews are faster

Epic 3: Cost Control

As a manager, I want cost estimates upfront so that I can approve budgets
As a developer, I want spending alerts so that I avoid surprise bills
As a finance team, I want cost tracking so that we can report AI spending

Non-Functional Requirements

Performance

NFR-1.1: Single iteration completes in < 5 minutes for typical tasks
NFR-1.2: Support tasks up to 50 iterations within 24 hours
NFR-1.3: Handle codebases up to 500,000 LOC
NFR-1.4: Process context windows up to 200K tokens

Reliability

NFR-2.1: 95% convergence rate on well-defined tasks
NFR-2.2: Zero data loss via git persistence
NFR-2.3: Graceful handling of API errors
NFR-2.4: Recovery from interrupted sessions

Security

NFR-3.1: No secrets in git history
NFR-3.2: Sandboxed execution for --dangerously-skip-permissions
NFR-3.3: Branch protection enforcement
NFR-3.4: Code review required before merge

Usability

NFR-4.1: Task definition template provided
NFR-4.2: Error messages are actionable
NFR-4.3: Progress visible in real-time
NFR-4.4: One-command installation

Scalability

NFR-5.1: Support parallel Ralph sessions
NFR-5.2: Handle enterprise-scale codebases
NFR-5.3: Multi-project orchestration
NFR-5.4: Team collaboration on Ralph tasks

Success Metrics

Adoption Metrics

Users running Ralph weekly: Target 70%+ of team
Tasks completed autonomously: Target 40%+ of backlog
Overnight sessions: Target 3+ per week per developer

Quality Metrics

Task success rate: Target >= 85%
False completion rate: Target < 5%
Code review rejection rate: Target < 10%

Efficiency Metrics

Developer time saved: Target 10+ hours/week
Cost per completed task: Target < $100
ROI: Target >= 500%

Satisfaction Metrics

Developer NPS: Target >= 50
Would recommend: Target >= 80%
Continued usage after 30 days: Target >= 90%

Dependencies & Assumptions

Technical Dependencies

Claude Code installed and authenticated
Git repository with proper configuration
Automated test suite in place
CI/CD pipeline for verification
API access with sufficient quota

Assumptions

Tasks are well-defined and measurable
Codebases have automated tests
Developers understand git workflows
Teams accept autonomous AI coding
Cost structure remains viable

Risks & Mitigations

Risk 1: Runaway Costs

Probability: Medium
Impact: High
Mitigation: Mandatory iteration limits, spending alerts, approval gates

Risk 2: Low-Quality Code

Probability: Medium
Impact: Medium
Mitigation: Strong verification, code review, automated quality checks

Risk 3: Security Vulnerabilities

Probability: Low
Impact: High
Mitigation: Security scanning, sandboxed execution, review processes

Risk 4: Developer Resistance

Probability: Medium
Impact: Medium
Mitigation: Training, success showcases, gradual adoption

Release Plan

Phase 1: Pilot (Weeks 1-4)

3-5 early adopter developers
Simple, low-risk tasks only
Manual cost monitoring
Daily feedback sessions

Phase 2: Team Rollout (Weeks 5-8)

Full development team
Broader task types
Automated cost tracking
Weekly retrospectives

Phase 3: Production (Weeks 9-12)

Organization-wide availability
Complex, high-value tasks
Full automation
Continuous improvement

Open Questions

How do we handle tasks requiring domain expertise?
What's the policy for code generated overnight?
Who approves high-cost Ralph runs (>$500)?
How do we measure long-term code quality impact?
What's the escalation path for failed Ralph sessions?

Appendix

Stakeholders

Development Team: Primary users
Engineering Leadership: Approval and budget
Security Team: Risk assessment
Finance: Cost tracking and reporting

Approval Required:

Engineering Lead
Security Review
Finance Approval
Developer Representatives