Imagine a coding assistant that seamlessly adapts to your every edit, predicting your next move before you even type it. That future is closer than you think. New research explores how Large Language Models (LLMs) can instantly update their predictions as you modify your code, solving a major efficiency bottleneck in AI-powered coding tools. Traditional LLMs have to re-process the entire codebase with each change, a time-consuming roadblock for real-time assistance. This new approach, called Positional Integrity Encoding (PIE), offers a clever workaround. Building on existing methods, PIE refocuses the LLM by quickly adjusting its internal representation of the code’s structure, bypassing the need for a full re-computation. The result? An AI coding buddy that keeps pace with your edits without the lag. Experiments show PIE reduces computational overhead by over 85% across various coding tasks and LLM sizes, all without sacrificing accuracy. This breakthrough promises more responsive and efficient AI coding assistants, making real-time code generation a practical reality. The research focuses on predicting lines of code in real-world coding scenarios, including tasks like inserting, deleting, and modifying code blocks. Tested on a large code dataset using several DeepSeek-Coder models, PIE consistently outperforms the traditional 'Conflict Fast Encoding' method, showing a remarkable ability to maintain prediction accuracy even with complex, multi-stage edits. This research tackles the core challenge of making LLMs practical partners in live coding sessions. The focus on optimizing how LLMs handle real-time changes paves the way for more dynamic, interactive, and truly helpful AI coding tools.