SSPO | Self-Supervised Prompt Optimization

A Self-Supervised Prompt Optimization (SSPO) implemented in TypeScript. Research Paper

✨ Features

- 🏷️ Zero Supervision - No ground truth/human feedback required
- ⚡ Universal Adaptation - Closed & open-ended tasks supported
- 🔄 Self-Evolving - Auto-optimization via LLM-as-judge mechanism

🚀 Quick Start

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- Node.js
- npm
- OpenAI API key

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``bash
npm install
npm run build
`

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Configure LLM parameters in src/config/config.yaml:

`yaml
models:
gpt-4o-mini:
api_key: "your-api-key-here"
base_url: "https://api.openai.com/v1"
temperature: 1
top_p: 1
`

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Create a template file in src/settings/template.yaml:

`yaml
prompt: |
Please solve the following problem.

requirements: |
Generate more detailed explanations and use clear reasoning steps.

count: 50

qa:
- question: |
What is 2 + 2?
answer: |
4

- question: |
Explain photosynthesis.
answer: |
Photosynthesis is the process by which plants convert sunlight into energy...
`

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#### Option 1: Command Line Interface

`bash


Basic usage

npm run optimize

With custom parameters

npm run optimize -- \
--template template.yaml \
--name project-1 \
--max-rounds 5 \
--opt-model gpt-4o-mini \
--eval-model gpt-4o-mini
`

Available options:
`bash
--opt-model Model for optimization (default: gpt-4o-mini)
--opt-temp Temperature for optimization (default: 0.7)
--eval-model Model for evaluation (default: gpt-4o-mini)
--eval-temp Temperature for evaluation (default: 0.3)
--exec-model Model for execution (default: gpt-4o-mini)
--exec-temp Temperature for execution (default: 0)
--workspace Output directory path (default: workspace)
--initial-round Initial round number (default: 1)
--max-rounds Maximum number of rounds (default: 10)
--template Template file name (default: Poem.yaml)
--name Project name (default: Poem)
--mode Execution model mode: base_model or reasoning_model (default: base_model)
`

#### Option 2: Programmatic Usage

`typescript
import { SPO_LLM, PromptOptimizer } from './src';

// Initialize LLM settings
SPO_LLM.initialize(
{ model: 'gpt-4o-mini', temperature: 0.7 },
{ model: 'gpt-4o-mini', temperature: 0.3 },
{ model: 'gpt-4o-mini', temperature: 0 },
'base_model'
);

// Create and run optimizer
const optimizer = new PromptOptimizer(
'workspace', // Output directory
1, // Starting round
10, // Maximum optimization rounds
'poem', // Project name
'Poem.yaml' // Template file
);

await optimizer.optimize();
`

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`
workspace/
└── poem/
└── prompts/
├── results.json
├── round_1/
│ ├── answers.txt
│ └── prompt.txt
├── round_2/
│ ├── answers.txt
│ └── prompt.txt
└── ...
`

- results.json: Optimization history and success metrics
- prompt.txt: Optimized prompt for each round
- answers.txt: Generated outputs using the prompt

🛠️ Development

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`
src/
├── components/ # Core optimization components
│ ├── optimizer.ts # Main PromptOptimizer class
│ └── evaluator.ts # Evaluation logic
├── utils/ # Utility modules
│ ├── llmClient.ts # LLM interface and management
│ ├── dataUtils.ts # Data handling and persistence
│ ├── promptUtils.ts # Prompt file operations
│ ├── evaluationUtils.ts # Evaluation utilities
│ └── load.ts # Configuration loading
├── llm/ # LLM implementations
│ └── asyncLlm.ts # Async LLM client
├── prompts/ # Prompt templates
│ ├── optimizePrompt.ts
│ └── evaluatePrompt.ts
├── config/ # Configuration files
├── settings/ # Task templates
├── optimize.ts # CLI interface
└── index.ts # Main entry point
`

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`bash
npm run build # Compile TypeScript
npm run start # Run compiled application
npm run dev # Run with ts-node (development)
npm run optimize # Run CLI optimizer
npm run clean # Clean build directory
``

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.