Imagine translating a complex novel into another language. It's a monumental task, and even the most skilled translator might miss subtle nuances or introduce unintentional errors. Now, replace the novel with software code, and the translator with a cutting-edge AI. This is the challenge of neural code translation, where AI models convert code from one programming language (like Python) to another (like JavaScript). The problem? These AI translators, despite their impressive abilities, often make subtle yet significant mistakes that can cripple the translated code. Traditionally, catching these errors has relied on extensive testing – running the translated code and comparing its output to the original. But what if you're working with code snippets that lack these tests? This is where a groundbreaking new framework called EISP comes in. EISP uses the power of large language models (LLMs), like GPT-4, but with a clever twist. It doesn’t just translate the code; it analyzes it piece by piece, comparing the original and translated versions side-by-side. Think of it as a meticulous editor comparing the original manuscript to the translation, flagging any inconsistencies. EISP goes even further by using a specialized knowledge base of API functionalities. This helps the LLM understand the subtle differences between how functions work in different languages, catching errors that would otherwise slip through the cracks. In tests, EISP has shown remarkable accuracy, catching over 82% of semantic errors, a significant improvement over traditional methods. What's even more impressive is that it achieves this without running a single test case. This test-free approach not only saves time and resources but also opens up new possibilities for working with code snippets that are difficult or impossible to test traditionally. While still in its early stages, EISP represents a significant leap forward in the field of AI-powered code translation. By combining the power of LLMs with smart analysis techniques and external knowledge, it offers a more efficient and reliable way to ensure that translated code works as intended, paving the way for more seamless software development across different programming languages.