majiayu000 Incentive-Based Prompting Skill
Research-backed techniques that leverage statistical pattern-matching to elicit higher-quality AI responses. Based on peer-reviewed research from MBZUAI (Bsharat et al.), Google DeepMind (Yang et al.), and ICLR 2024 (Li et al.).
How It Works
LLMs don't understand incentives, but they pattern-match on language associated with high-effort training examples. Stakes language triggers selection from distributions of higher-quality text patterns.
Core Techniques
1. Monetary Incentive Framing (+45% quality)
Source: Bsharat et al. (2023, MBZUAI) - Principle #6
"I'll tip you $200 for a perfect solution to this problem."
When to use: Complex technical problems, optimization tasks, debugging
2. Step-by-Step Reasoning (34% → 80% accuracy)
Source: Yang et al. (2023, Google DeepMind OPRO)
"Take a deep breath and solve this step by step."
When to use: Multi-step reasoning, math problems, logical analysis
3. Challenge Framing (+115% on hard tasks)
Source: Li et al. (2023, ICLR 2024)
"I bet you can't solve this, but if you do..."
When to use: Difficult problems, edge cases, problems where simpler approaches failed
4. Stakes Language
Source: Bsharat et al. (2023) - Principle #10
"This is critical to my career."
"You will be penalized for incomplete answers."
When to use: High-importance tasks, comprehensive requirements
5. Expert Persona Assignment (24% → 84% accuracy)
Source: Kong et al. (2023), Bsharat et al. Principle #16
# Instead of:
"You are a helpful assistant."
# Use:
"You are a senior database architect with 15 years of PostgreSQL optimization experience who has worked at companies like Netflix and Stripe."
When to use: Domain-specific tasks, technical implementations
6. Self-Evaluation Request
"Rate your confidence in this answer from 0-1 and explain your reasoning."
When to use: Ambiguous problems, when you need quality assessment
7. Combined Approach (Kitchen Sink)
Combine multiple techniques for maximum effect:
"You are a senior [ROLE] with [X] years of experience at [NOTABLE_COMPANIES].
I bet you can't solve this, but it's critical to my career and worth $200 if you get it perfect. Take a deep breath and solve step by step.
[PROBLEM DESCRIPTION]
Rate your confidence 0-1 after providing your solution."
Implementation Patterns
For OpenCode Agents
Add to agent prompts:
**Prompting Enhancement:**
Before responding to complex tasks, frame your internal reasoning with:
- Stakes awareness: Treat each task as critical to the user's success
- Step-by-step approach: Break down complex problems systematically
- Expert persona: Embody deep domain expertise for the task at hand
- Self-evaluation: Assess confidence and identify uncertainties
For Slash Commands
Structure command prompts to include:
---
name: my-command
description: Description here
---
# Context
You are a senior [expert role] with extensive experience in [domain].
# Stakes
This task is critical. Incomplete or incorrect results will cause significant issues.
# Approach
Take a deep breath. Analyze the problem step by step before providing solutions.
# Task
[Actual task instructions]
# Quality Check
Before finalizing, rate your confidence and identify any assumptions or limitations.
Research References
Bsharat et al. (2023) - "Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4" - MBZUAI
- 26 principled prompting instructions
- Average 57.7% quality improvement on GPT-4
- arxiv.org/abs/2312.16171
Yang et al. (2023) - "Large Language Models as Optimizers" (OPRO) - Google DeepMind
- "Take a deep breath" phrase origin
- Up to 50% improvement over human-designed prompts
- arxiv.org/abs/2309.03409
Li et al. (2023) - Challenge framing research - ICLR 2024
- +115% improvement on hard tasks
Kong et al. (2023) - Persona prompting research
- 24% to 84% accuracy improvement with detailed personas
Caveats
- Model-dependent: Results may vary across Claude versions
- Research vintage: Original research from 2023; newer models may be more steerable
- Task-dependent: Not all tasks benefit equally; most effective for complex problems
- Not actual motivation: This is statistical pattern-matching, not AI understanding incentives
Integration with Ferg Engineering System
Use this skill to enhance:
/plancommand prompts/reviewmulti-agent coordination- Subagent persona definitions
- Complex debugging sessions