Guidelines for Structuring tables in technical writing for GenAI-based agents

Representation of complex information can be done effectively through text in the form of tables. With a simple yet powerful design, tables offer an elegant solution to distill vast amounts of data into a visually captivating format. Technical writers rely on tables to decipher key points, discern trends, and unveil relationships, empowering them to craft comprehensive documents. In the realm of technical writing, tables have become indispensable tools, revolutionizing the content creation process.

Overview of Table formats

Tables, composed of rows and columns, organize content with clarity. They effortlessly transform into multi-dimensional hierarchies, encapsulating vast amounts of data. From the fundamental table of contents to intricate indexes, tables summarize knowledge with clarity. Technical writers adapt tables to suit content types and user experience needs, favoring column tables for mobile interfaces and row tables for desktops.

Large Language Model Challenges

LLMs, trained on vast internet text, excel with unstructured data but face hurdles with tables. These structured formats challenge their processing capabilities, especially with numerical data. This complexity risks overshadowing vital details, hindering decision-making. Technical writers' preference for tabular data clashes with LLM limitations, impacting response generation. Refactoring tables becomes crucial for LLMs to grasp structured information effectively.

Refactoring tables for LLMs

The tables inside your knowledge base content need to be refactored to make it suitable for ingestion by business applications powered by Large Language Models (LLMs). The following are some of the best practices to be followed while refactoring the table

• Do not use symbols inside the table content as they are removed during pre-processing steps

• Do not have null values / empty spaces inside your table content as GenAI-based agents might hallucinate while trying to use those data!
• Ensure that tables have header information along with proper rows
• If you wish to have some binary information part of the table content, use Yes/No, True/ False, or any other option. Ensure that this information is covered in the system message of your RAG (Retrieval Augmented Generation) tool
• The table should be complete such that all cells have values in them
• Use < abbr > tag to define abbreviations of terms inside the table content
• Use < abbr > tag to describe tick mark and cross mark so that LLMs can understand the meaning of symbols inside the table content
• Table cell values can be a mix of numeric values and text. However, it is recommended to have one type of data present inside those table cells

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