In an evolving digital landscape where developers increasingly rely on AI agents to navigate documentation, optimizing documents for large language models (LLMs) is crucial for accurately conveying API information. Many developers face challenges due to unoptimized documentation, which leads to incorrect AI-generated responses, support ticket inflation, and onboarding friction. The text outlines strategies to enhance LLM compatibility by maintaining a predictable heading structure, using consistent terminology, properly formatting code examples, providing text alternatives for visual content, and being explicit in descriptions. Addressing semantic drift and periodically auditing documentation for consistency are recommended to prevent misinformation. Testing documentation with AI tools and focusing on high-impact content can ensure improved accuracy and efficiency. Platforms like Redocly, which generate documentation from OpenAPI specs, are highlighted as effective solutions for creating AI-friendly documentation without sacrificing human readability.