AI Course | How to Optimize your Files for RAG: Data Structuring
This video explores the foundational steps for preparing data for Retrieval-Augmented Generation (RAG), focusing on document formatting and structure. Proper file types and a logical organization of content are critical for ensuring that an AI agent can accurately process and retrieve information.
Supported file formats include PDFs, Word documents, HTML, Markdown, and plain text. File types that cannot be easily parsed, such as image-based documents with complex formatting, are not suitable for use as a knowledge base, as they limit an agent’s ability to generate reliable responses. Botpress automatically converts uploaded files into Markdown format, enabling consistent handling by the AI agent. For optimal results, raw Markdown files or Rich Text knowledge base types are recommended.
Document structure plays a significant role in enhancing an agent's comprehension. Using clear and logical sections, with titles, headings, and subheadings, helps an LLM categorize and retrieve relevant information more effectively. A well-structured document also ensures that vectorization processes group related content correctly, reducing inconsistencies in retrieval performance.
Organizing and standardizing files improves data quality, making it easier for AI agents to process and retrieve accurate information.
You can learn more about Botpress at https://botpress.com