Imagine building a house with tools that become obsolete halfway through the project. That's the risk developers face when Large Language Models (LLMs) recommend outdated APIs, which can introduce security vulnerabilities into their code. LLMs, like any AI, learn from a fixed dataset. This dataset can quickly become outdated in the fast-paced world of software, where APIs—the building blocks of code—are constantly evolving, sometimes to patch critical vulnerabilities. This means an LLM might unknowingly suggest using an API with a known security flaw, leaving your code exposed. To solve this challenge, researchers have developed APILOT, a real-time "navigator" for LLMs. APILOT keeps track of changes within software packages, identifying outdated or vulnerable APIs as they emerge. Think of it as a constantly updated safety guide for your LLM, steering it away from potential coding hazards. When an LLM is about to use an outdated API, APILOT intervenes, providing updated, safe recommendations and ensuring the generated code remains secure and functional, regardless of the developer's software version. APILOT has proven highly effective, significantly reducing the use of outdated APIs in generated code by nearly 90% in tests across seven different LLMs, including popular models like GPT-3.5. Moreover, it improves the usability of the generated code. While APILOT currently focuses on Python, its modular design allows for potential expansion to other programming languages. The implications of this research go beyond just generating secure code with LLMs. The technology behind APILOT could be integrated into developer tools to flag outdated API usage in real-time, creating a more secure development environment from the ground up. APILOT represents an essential step toward harnessing the power of LLMs while mitigating the security challenges that come with an ever-changing software landscape.