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This article outlines optimal fixed-size chunking strategies for OpenAI-based Retrieval-Augmented Generation (RAG) systems using SaaS product documentation. It categorizes common documentation types—API references, tutorials, onboarding guides, concept overviews, and FAQs—and recommends chunk sizes, overlap, and expected chunk counts for each, all within OpenAI's token constraints. Smaller chunks enhance precision for structured docs like APIs and FAQs, while larger chunks preserve context in tutorials and conceptual guides. Strategic overlap ensures continuity without redundancy. The study also offers guidance on improving documentation structure to enhance AI retrieval accuracy, ensuring effective support chatbots and file search experiences built on OpenAI models.