promptfletcher 0.2.0

PromptFletcher is a lightweight, deterministic auto-prompt engineering library for NLP and LLM workflows.

PromptFletcher 🚀

Deterministic Auto-Prompt Engineering for Python & NLP

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Overview

PromptFletcher is a lightweight, deterministic, and dependency-minimal Python library for automatic prompt refinement using classical NLP heuristics.

Unlike LLM-based prompt optimizers, PromptFletcher:

• Does not require external APIs
• Does not use large transformer models
• Works offline
• Produces reproducible results
• Is fast enough for CI, batch jobs, and research pipelines

It is ideal for developers, researchers, and teams who want structured prompt improvement without LLM overhead.

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Key Capabilities

• Heuristic-based prompt evaluation
• Iterative prompt refinement
• Context-aware relevance scoring
• Fast execution using NLTK
• Deterministic and reproducible behavior
• Minimal runtime footprint

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Installation

Install from PyPI (recommended)

pip install promptfletcher

Install from GitHub (latest)

pip install git+https://github.com/Vikhram-S/PromptFletcher.git

On first use, required NLTK resources are downloaded automatically.

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Quick Start

Initialize the Engine

from promptfletcher import AutoPromptEngineer

engineer = AutoPromptEngineer()

Define Context and Prompt

context = "We are optimizing prompt quality for large language models." prompt = "How improve AI responses"

Refine the Prompt

refined = engineer.refine_prompt(prompt, context) print(refined)

Example output:

How improve AI responses? Please explain in detail.

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How It Works

PromptFletcher applies classical NLP heuristics to evaluate and refine prompts:

Heuristic	Description
Length	Penalizes prompts that are too short or too verbose
Clarity	Rewards explicit questions
Relevance	Measures keyword overlap with context
Iteration	Keeps only prompt improvements

This design makes the system predictable, explainable, and easy to debug.

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API Reference

AutoPromptEngineer

refine_prompt(prompt: str, context: str, iterations: int = 3) -> str
Refines a prompt iteratively using heuristic scoring.

evaluate_prompt(prompt: str, context: str) -> float
Returns a numeric quality score for a prompt.

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Use Cases

• Prompt standardization in teams
• Automated prompt cleanup pipelines
• Prompt benchmarking
• Research experiments
• CI validation of prompts
• Offline prompt tuning
• Educational NLP projects

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Dependencies

PromptFletcher keeps dependencies intentionally minimal:

• nltk >= 3.6
• numpy >= 1.21
• regex >= 2023.3.23

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License

PromptFletcher is released under the MIT License.
You are free to use, modify, and distribute this software.

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Contributing

Contributions are welcome.

Workflow:

1. Fork the repository
2. Create a branch (feature/your-feature)
3. Commit your changes
4. Push and open a Pull Request

Please ensure code is typed, documented, and non-breaking.

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Support and Contact

Issues: https://github.com/Vikhram-S/PromptFletcher/issues
Email: [e-mail]

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Support the Project

If PromptFletcher helps you:

• Star the repository
• Use it in your projects
• Share it with others

Your support helps grow the project.