leo-prompt-optimizer 0.1.4

A Python library to optimize prompt drafts using LLMs

🧠 leo-prompt-optimizer

leo-prompt-optimizer is a Python library that helps developers optimize raw LLM prompts into structured, high-performance instructions using real LLM intelligence.

It leverages open-source models via Groq API (like Mixtral or LLaMA 3), making it fast, affordable, and production-ready.

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🚀 Features

• 🛠️ Refines vague, messy, or unstructured prompts
• 🧠 Follows a 9-step prompt engineering framework
• 🧩 Supports contextual optimization (with user input & LLM output)
• ⚡ Runs on blazing-fast open-source LLMs via Groq
• 🔐 Secure API key management with .env or helper function

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📦 Installation

pip install leo-prompt-optimizer

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🔧 Setup: API Key

You can provide your Groq API key in two ways:

✅ Option A: .env file (recommended)

At the root of your project, create a .env file:

GROQ_API_KEY=sk-your-api-key-here

Then, in your Python script (before calling the optimizer), load the .env file:

from dotenv import load_dotenv
load_dotenv()  # 👈 Required to make the .env key visible

from leo_prompt_optimizer import optimize_prompt

optimized = optimize_prompt("Your draft prompt here")

⚠️ Important: load_dotenv() must be called before importing or using the optimizer, or the key won’t be found.

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✅ Option B: Set programmatically

Useful in notebooks, scripts, or cloud functions:

from leo_prompt_optimizer import set_groq_api_key, optimize_prompt

set_groq_api_key("sk-your-api-key")

optimized = optimize_prompt("Your draft prompt here")

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✍️ Usage Example

🔹 Minimal use (just the prompt draft)

from dotenv import load_dotenv
load_dotenv()

from leo_prompt_optimizer import optimize_prompt

draft = "I want to generate a structured planning for a GenAI course with communication adapted to enrolled members."

optimized = optimize_prompt(draft)
print(optimized)

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🔹 With optional user_input and llm_output

You can provide additional context to help the optimizer refine the prompt even better:

from leo_prompt_optimizer import set_groq_api_key, optimize_prompt

set_groq_api_key("sk-your-api-key")

draft = "Help me create a better prompt to extract user feedback from a UI test session."

user_input = "The user said the interface was 'cluttered and confusing' but didn't explain why."
llm_output = "You could ask: 'Which parts feel confusing?' or 'What would make it feel more intuitive?'"

optimized = optimize_prompt(draft, user_input=user_input, llm_output=llm_output)
print(optimized)

🧠 user_input and llm_output are optional, but highly recommended when refining an existing prompt flow.

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📘 Output Format

The returned optimized prompt follows a structured format:

Role:
[Define the LLM's persona]

Task:
[Clearly state the specific objective]

Instructions:
* Step-by-step subtasks

Context:
[Any relevant background, constraints, domain]

Output Format:
[e.g., bullet list, JSON, summary]

User Input:
[Original user input or example]

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🧪 Quick Test (Optional)

You can run a built-in test script after installation to validate that everything works:

python3 test_import.py

This will check:

• ✅ Import is working
• ✅ API key is detected
• ✅ LLM call returns a result

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🧯 Common Errors & Fixes

Error	Solution
Missing GROQ_API_KEY	Ensure it's either in .env and loaded with load_dotenv(), or passed via set_groq_api_key()
Invalid model or 403	The default model might be deprecated. Check Groq’s model list
ModuleNotFoundError	Ensure leo-prompt-optimizer is installed in the current environment

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💡 Why Use It?

Prompt quality is critical when building with LLMs.

leo-prompt-optimizer helps you:

• ✅ Make prompts explicit and structured
• 🚫 Reduce hallucinations
• 🔁 Increase consistency and repeatability
• 🧱 Standardize prompt formats across your stack

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📄 License

MIT © 2025 [Léonard Baesen-Wagner]