AI language models are magicians with prose, but a lot of the world’s useful information isn’t particularly well-formed into paragraphs. It is trapped in tables, forms, charts, scanned documents and complex layouts like comic strips or pages of math equations. The challenge of teaching AI to reliably interpret this structured and semi-structured information and extract insights from it is critical, and new tools have made impressive strides. However, standard LLMs do not perform well on formatting-pertaining instructions. They might read the rows of a table as an uninterrupted set of sentences, for example, or be confused by the spatial relations among elements on a page. This significantly reduces their relevance to most business processes which focus on extracting data from invoices, reports, or legacy documents. Enter specialized tools and techniques. Examples of these would include recently Llama-Parse, released by the LlamaIndex team, which is specifically for reasoning over formatted text. Its goal is to assist AI models in correctly interpreting tables, processing the flow of graphical novels, or comprehending equations in context. Another approach suggested could involve multimodal models which take in not just the layout, but also the text, or an OCR (Optical Character Recognition) engine trained with template models combined with complex parsing algorithms. The ability for AI to accurately read and reason over complex documents opens up incredible value. It permits automatic data input, quicker interpretation of financial accounts, more correct audits of scientific material, and heightened access for visually dense content. As these tools evolve, they connect the vast linguistic ability of AI to the myriad formats in which human knowledge is stored, transforming mountains of previously off-limits information into fodder for AI-enabled analysis and automation.