Backprompt: AI Integration, Simplified

Tailor LLM outputs to your exact needs, no datasets or finetuning required.

Installation

pip install backprompt

Usage

Step 1: Solve for an optimal prompt

from backprompt import BackpromptClient

# Initialize the client
client = BackpromptClient(
    api_key='[your-api-key]', 
    api_url='[your-api-url]'
)


input_text = "The new smartphone boasts a 108MP camera and 5G capability."
desired_output = """
🌟 Tech Specs Breakdown 🌟
📸 Camera: 108MP (Ultra High-Res)
📡 Connectivity: 5G-enabled
💡 Key Benefit: Pro-level photography with lightning-fast uploads
"""

optimal_prompt = client.solve_prompt(input_text, desired_output)
print(f"Optimal prompt: {optimal_prompt}")

Step 2: Deploy the optimal prompt

optimal_prompt.deploy(client)

Step 3: Generate using the deployed prompt

prompt_vars = {"input_data": "The latest smartwatch features a 1.4-inch AMOLED display and 7-day battery life."}
completion = optimal_prompt.run(client, prompt_vars)

print(f"Generated response: {completion}")

Key Features

1. Prompt Optimization: Generate optimal prompts based on input-output pairs.
2. Prompt Deployment: Deploy optimized prompts for quick access.
3. Response Generation: Generate responses using deployed prompts.

Backprompt's Edge for Developers

• Prompt Engineering, Automated
• Rapid Iteration Cycle
• Model-Agnostic Customization
• Resource Efficiency

Backprompt Across Domains

• Nuanced Sentiment Extraction
• Specialized Knowledge Base Q&A
• Consistent Code Snippet Generation

For more information, visit backprompt.ai

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