Imagine unlocking the secrets of a codebase without ever having to train a model. That's the promise of zsLLMCode, a groundbreaking new approach to generating functional code embeddings. In the ever-evolving landscape of software engineering, understanding and processing code efficiently is paramount. Traditional methods often rely on resource-intensive training, hindering their scalability and adaptability. zsLLMCode addresses this challenge by leveraging the power of Large Language Models (LLMs) like GPT-3.5, GLM3, and GLM4, combined with the precision of sentence-embedding models. The process is remarkably simple yet effective. First, an LLM summarizes the functionality of a code fragment in a single, concise sentence. This step bypasses the context length limitations that often plague LLMs when dealing with larger code segments. Then, a sentence-embedding model transforms this summary into a functional code embedding – a vector representation capturing the essence of the code’s purpose. This novel approach offers significant advantages. It eliminates the need for training data, making it highly efficient and adaptable to various programming languages like C and Java. Plus, by separating the summarization and embedding processes, zsLLMCode minimizes the risk of LLM “hallucinations” – instances where the model generates inaccurate or nonsensical output. Experimental results on datasets like OJClone and BigCloneBench show that zsLLMCode outperforms existing unsupervised methods in both code-clone detection and code clustering tasks. Visualizations of the embeddings reveal clear cluster separations, further validating the quality and effectiveness of this method. zsLLMCode opens up exciting new possibilities for various software engineering tasks. Imagine effortlessly searching for similar code snippets across massive codebases or automatically grouping related functions without manual labeling. While the current research focuses on specific LLMs and sentence-embedding models, the modular design of zsLLMCode allows for seamless integration with future advancements in AI, ensuring its continued relevance in this rapidly evolving field. The future of code analysis is here, and it's smarter, faster, and more efficient than ever before.