Boost AI Prompt Quality with Prompt Optimizer: Features, Docker Setup & Real‑World Demo

Introduction

Prompt Optimizer is a powerful prompt‑optimization tool that helps you write better AI prompts. It already has over

2k+star

on GitHub.

Features

Smart optimization: one‑click prompt optimization with multi‑round iteration.

Comparison testing: shows original vs optimized prompts.

Supports multiple large models: DeepSeek, OpenAI, Gemini, etc.

Secure architecture: client‑side processing only.

Privacy protection: local encrypted storage of API keys and history.

Multi‑platform: web app and Chrome extension.

User interface: clean and intuitive design.

Below are screenshots of Prompt Optimizer’s interface.

Installation

Using Docker to install Prompt Optimizer is very convenient.

Pull the Docker image:

<code>docker pull linshen/prompt-optimizer</code>

Run the container:

<code>docker run -p 8020:80 --name prompt-optimizer -d linshen/prompt-optimizer</code>

After successful start, access the UI at http://192.168.3.101:8020

Practical Project

The article shares a real‑world e‑commerce system built with SpringBoot3 + Vue, featuring a microservice architecture deployed with Docker and Kubernetes. Links to the Boot project, Cloud project, and video tutorials are provided.

Usage

Prompt Optimizer supports multiple themes; the author prefers the night theme.

Model settings can be changed via the top‑right “Model Settings” button; the example uses the Alibaba Cloud Baichuan model

deepseek-r1

.

Enter an original prompt such as “Create a complete SpringBoot learning roadmap” to get an optimized version.

Various built‑in optimization prompts are available for selection.

Compare original and optimized prompts in the test area; optimized results are more detailed and aligned with needs.

The output is in Markdown format, ready to copy into a Markdown editor.

Conclusion

Prompt Optimizer helps generate more accurate prompts, leading to better answers from DeepSeek and improving work efficiency.

Project Links

GitHub: https://github.com/linshenkx/prompt-optimizer (11K stars). The related microservice project mall‑swarm has 60K stars and comprehensive video tutorials (~26 hours, 59 episodes) covering Spring Cloud, microservices, and Kubernetes.