What is “Slop”?
“Slop” refers to low-quality LLM output patterns:- Excessive verbosity - 10 paragraphs when 2 sentences suffice
- Unnecessary preambles - “Great question! I’d be happy to help…”
- Filler content - Restating the question, obvious observations
- Over-hedging - “It depends”, “There are many factors…”
- Emoji abuse - Unprompted decorative emojis
- Repetition - Saying the same thing multiple ways
Why It Matters
Slop wastes tokens, time, and attention:Constraint Techniques
Direct Instructions
Add anti-slop rules to prompts:Configuration by Tool
Add anti-slop rules to your AI tool’s config file:- Claude Code
- Gemini CLI
- OpenCode
- Cursor
- GitHub Copilot
- Windsurf
- OpenAI API
File:
CLAUDE.md (project root or ~/.claude/)Structured Output Requests
Force structure to prevent rambling:Anti-Patterns to Block
The Preamble Problem
The Summary Trap
The Hedge Spiral
Measurement
Token Efficiency Ratio
Quality Signals
Implementation
Agent Prompt Template
Review Checklist
Before accepting agent output:- Starts with substance, not preamble
- No filler phrases
- No unnecessary hedging
- No emoji spam
- Appropriate length for content
- No repetition or restating
- No meta-commentary (“In this response…”)
Calibration
When Verbosity is Appropriate
Slop constraints don’t mean terse at all costs:- Teaching contexts - Explanations need detail
- Critical decisions - Nuance matters
- User-facing content - Personality is valuable
- Complex topics - Brevity can mislead
Adjusting by Context
Common Fixes
Fix Over-Qualification
Fix Unnecessary Structure
Fix Explanation Creep
Best Practices
- Add anti-slop rules to all agent prompts
- Review output token efficiency regularly
- Use structured output formats
- Train on examples of ideal responses
- Measure and track verbosity metrics
Related
Token Economics
Reduce costs through efficiency
Context Optimization
Quality input = quality output