The tidal wave of advancements in Large Language Models (LLMs) has led to their swift integration into application-level logic. Many software systems now use prompts to interact with these black-box models, combining natural language with dynamic values interpolated at runtime, to perform tasks ranging from sentiment analysis to question answering. Due to the programmatic and structured natural language aspects of these prompts, we refer to them as Developer Prompts. Unlike traditional software artifacts, Dev Prompts blend natural language instructions with artificial languages such as programming and markup languages, thus requiring specialized tools for analysis, distinct from classical software evaluation methods. In response to this need, we introduce PromptDoctor, a tool explicitly designed to detect and correct issues of Dev Prompts. PromptDoctor identifies and addresses problems related to bias, vulnerability, and sub-optimal performance in Dev Prompts, helping mitigate their possible harms. In our analysis of 2,173 Dev Prompts, selected as a representative sample of 40,573 Dev Prompts, we found that 3.46% contained one or more forms of bias, 10.75% were vulnerable to prompt injection attacks. Additionally, 3,310 were amenable to automated prompt optimization. To address these issues, we applied PromptDoctor to the flawed Dev Prompts we discovered. PromptDoctor de-biased 68.29% of the biased Dev Prompts, hardened 41.81% of the vulnerable Dev Prompts, and improved the performance of 37.1% sub-optimal Dev Prompts. Finally, we developed a PromptDoctor VSCode extension, enabling developers to easily enhance Dev Prompts in their existing development workflows. The data and source code for this work are available at

Quantization is the process of mapping a large set of input values to a smaller set of output values, often integers. It is a key technique in digital signal processing where continuous signals are mapped to discrete digital values, and it reduces the data's precision to make storage and computation more efficient while attempting to retain essential information. With the development of Large Language Models (LLMs), the models have grown extremely large, so the memory usage and inference speed are greatly limited by the size of the model. Consequently, as one of the most popular technique for model compression, quantization has many variants now used for LLM compression and inference acceleration. The goal of quantization in LLMs is to reduce their size while minimizing its influence on inference speed.