Gitingest`, a tool that converts public GitHub repositories into text files suitable for Large Language Models (LLMs), helps improve the accuracy of AI assistants by providing them with a reliable "digest" of codebase information, API references, and domain-specific documentation. `Cursor`, another tool, features a native `@docs` command that allows users to reference external documentation seamlessly, reducing context switching and hallucinations. A proposed standard, `/llms.txt`, aims to provide a centralized location for key site content, making it easier for LLMs to quickly find and use relevant information. Additionally, tools like Replit's Ghostwriter analyze workspaces in real-time, providing autocompletion, documentation, and error correction features that leverage project context to improve developer productivity. By feeding high-quality data into LLMs, developers can reduce hallucinations, accelerate workflows, and standardize best practices, ultimately supporting the idea that context is king for these AI-powered tools.