When should I use the LLM Prompt Optimizer?

Use it when you are scaling an application, experiencing high latency, or want to refine complex instructions for better results.

Will optimizing my prompt lower the quality of the output?

No, the skill is designed to maintain or improve output quality by making instructions more direct and clear for the model.

How does this skill reduce LLM costs?

It analyzes prompts to remove verbose language and redundancy, resulting in fewer input tokens and lower billing per request.

Does it work with specific models?

Yes, it applies general prompt engineering best practices that benefit most modern LLMs, including Claude and GPT models.

Can I see what changes were made to the prompt?

Yes, the skill provides the optimized prompt along with an explanation of the specific changes and their expected impact on performance.

LLM Prompt Optimizer

Name: LLM Prompt Optimizer
Author: intent-solutions-io

byintent-solutions-io

データサイエンスとML

Optimizes LLM prompts to reduce token consumption, lower API costs, and improve response quality through automated prompt engineering.

概要

This skill empowers developers to maximize the efficiency of Large Language Models by refining input prompts for optimal performance. By identifying redundancies and applying concise phrasing, it helps reduce token usage and associated costs while maintaining or enhancing the clarity and accuracy of LLM outputs. It is particularly useful for high-volume production environments where cost management and response latency are critical factors in maintaining a competitive AI-driven service.

主な機能

Detailed explanations of optimization changes
Redundancy identification and removal
Integration with specialized prompt architecture agents
0 GitHub stars
Token usage reduction through concise rewriting
Prompt performance and clarity enhancement

ユースケース

Reducing recurring API costs for high-traffic LLM applications
Standardizing prompts for complex data summarization or content generation
Improving response speed by minimizing input token processing

概要

主な機能

Detailed explanations of optimization changes
Redundancy identification and removal
Integration with specialized prompt architecture agents
0 GitHub stars
Token usage reduction through concise rewriting
Prompt performance and clarity enhancement

ユースケース

Reducing recurring API costs for high-traffic LLM applications
Standardizing prompts for complex data summarization or content generation
Improving response speed by minimizing input token processing