leo-prompt-optimizer 0.2.1

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 supports both Groq (LLaMA, Mixtral, etc.) and OpenAI (GPT-3.5, GPT-4) — making it fast, flexible, 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)
• 🔁 Works with both Groq and OpenAI
• ⚡ Blazing-fast open models via Groq
• 🔐 Secure API key management with .env or helper functions
• 🎛️ Lets users pick the model (gpt-4, llama3-70b, etc.)

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📦 Installation

pip install leo-prompt-optimizer

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🔧 Setup: API Keys & Client

You can provide API keys either via .env or programmatically, then set the provider.

✅ Option A: Use .env (recommended)

Create a .env file in your project root:

GROQ_API_KEY=sk-your-groq-key
OPENAI_API_KEY=sk-your-openai-key

Then in Python:

from dotenv import load_dotenv
load_dotenv()

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✅ Option B: Set manually in code

from leo_prompt_optimizer import set_groq_api_key, set_openai_api_key

set_groq_api_key("[YOUR GROQ KEY]")
set_openai_api_key("[YOUR OPENAI KEY]")

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✅ Set your provider

Before calling optimize_prompt, set the backend provider:

from leo_prompt_optimizer import set_client

# For Groq (default)
set_client(provider="groq")

# For OpenAI (with optional custom base URL)
set_client(provider="openai", base_url="https://openrouter.ai/v1")

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✍️ Usage Example with key in .env

from dotenv import load_dotenv
load_dotenv()

from leo_prompt_optimizer import (
    optimize_prompt,
    set_groq_api_key,
    set_openai_api_key,
    set_client
)

# Required: set backend provider
set_client(provider="groq")  # or "openai"
# or if you have a specific base_url : 
set_client(provider="openai", base_url="[YOUR BASE URL]") 

# Prompt example
optimized = optimize_prompt(
    prompt_draft="[YOUR PROMPT]",
    user_input_example="[POTENTIAL INPUT EXAMPLE]", # Optional
    llm_output_example="[POTENTIAL OUTPUT EXAMPLE]", # Optional
    top_instruction="[POTENTIAL EXTRA CONTEXT, SPECIFIC INSTRUCTION TO FOCUS ON FOR THE LLM]", # Optional
    model="[YOUR MODEL]",            # Optional: model choie based on your provider(e.g. "gpt-4", "llama3-70b", etc.)
)

print(optimized)

🧠 user_input_example and llm_output_example are optional but improve accuracy. 💬 top_instruction is optional — it adds specific guidance to influence the optimization (e.g., tone, audience, format).

🎛️ model is optional — defaults are:

• "openai/gpt-oss-20b" for Groq
• "gpt-oss-120b" for OpenAI

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📘 Output Format

Optimized prompts follow a clear, modular structure:

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)

python3 test_import.py

Checks that:

• ✅ Import works
• ✅ API keys are found
• ✅ LLM returns a response

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🧯 Common Errors & Fixes

Error	Solution
Missing API key	Add key to .env or use set_groq_api_key() / set_openai_api_key()
Client not set	Use set_client("groq") or set_client("openai") before calling optimize_prompt()
Invalid model or 403	Model may be deprecated or not available — try another, or check Groq’s model list
ModuleNotFoundError	Check if leo-prompt-optimizer is installed in the correct virtual environment

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💡 Why Use It?

Prompt quality is critical for any GenAI product.

leo-prompt-optimizer helps you:

✅ Make prompts explicit and modular 🚫 Reduce hallucinations 🔁 Improve consistency and reuse 🧱 Standardize prompt formats across your stack

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