AI Integration, Simplified. Tailor LLM outputs to your exact needs, no datasets or finetuning required.
Project description
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='sk-675ef0e4-6604-47e2-943d-b62379040a90',
api_url='http://localhost:8001'
)
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
- Prompt Optimization: Generate optimal prompts based on input-output pairs.
- Prompt Deployment: Deploy optimized prompts for quick access.
- 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
© 2024 backprompt
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