muratcankoylan

Intent Based Prompt Calibration

Testing community

SOURCE https://github.com/muratcankoylan/Intent-based-Prompt-Calibration

Description

A system for automatic prompt optimization using Claude 3.5 models. IPC enables dynamic prompt refinement through iterative testing and improvement cycles, particularly useful for complex tasks like content moderation.

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README

Intent-based Prompt Calibration (IPC)

A system for automatic prompt optimization using Claude 3.5 models. IPC enables dynamic prompt refinement through iterative testing and improvement cycles, particularly useful for complex tasks like content moderation.

Features

Automatic prompt optimization through systematic testing
Synthetic test case generation for edge cases
Comprehensive error analysis and tracking
Multi-model architecture using Claude 3.5 models
Simple configuration and usage

Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases: https://arxiv.org/abs/2402.03099

Quick Start

from ipc_system import AdvancedIPCConfig, EnhancedIPCSystem
import anthropic

# Initialize client
client = anthropic.Anthropic(api_key="your-api-key")

# Configure system
config = AdvancedIPCConfig(
    task_description="Your task description",
    labels=["label1", "label2"],
    initial_prompt="Your initial prompt"
)

# Run IPC
ipc = EnhancedIPCSystem(client, config)
best_prompt, history = ipc.calibrate()