# Making AI-generated Code More Accurate in Any Language  ## Metadata - Author: [[Adam Zewe]] - Full Title: Making AI-generated Code More Accurate in Any Language - Category: #articles - Summary: Researchers from MIT have developed a new technique that helps large language models (LLMs) generate error-free code in various programming languages. This method efficiently guides the LLMs to focus on the most promising outputs, allowing smaller models to outperform larger ones. The approach could enable non-experts to create complex queries and enhance AI tools for programming and data analysis. - URL: https://news.mit.edu/2025/making-ai-generated-code-more-accurate-0418 ## Highlights - They accomplish this using a technique called sequential Monte Carlo, which enables parallel generation from an LLM to compete with each other. The model dynamically allocates resources to different threads of parallel computation based on how promising their output appears. Each output is given a weight that represents how likely it is to be structurally valid and semantically accurate. At each step in the computation, the model focuses on those with higher weights and throws out the rest. ([View Highlight](https://read.readwise.io/read/01js55j7gc91a7dagezpe1nd1q))