This tutorial is part of the skills labs within Interactive Data Science and Visualization.

AI Advanced

Other formats: Markdown version

Using advanced AI techniques for data visualization including llms.txt, agentic workflows / subagents, and task files.
Contents

Motivation

We've just seen how we can chat with an AI assistant in building simple visualizations but let's consider two potential challenges and two solutions.

Knowledge: What if you ask the AI assistant about a technology it perhaps doesn't know much about or maybe where that technology has changed since when the AI was built? There is a protocol called llms.txt which lets us provide new knowledge to assistants. It is kind of like a library for AI!

Context: What about building visualizations with novel representations or lots of customization where there's quite a bit more code or iteration? AI assistants have limited "context" or chat lengths. This can sometimes impair our ability to use them in more complex tasks. That said, some assistants like Claude allow for use of "subagents" which you can think of as allowing multiple AI assistants to coordinate with each other, saving any individual from over-burdening their context.

Reproducibility: If we have a lot of work with an AI, how can we document our work? We will discuss markdown and the use of "task files" that allow us to prompt assistants with more structure and documentation.

This tutorial will walk through use of llms.txt and, optionally, use of assistants to build out a more bespoke / tailored version of the wolves and moose visualization from the previous tutorial.

Choose Your Path

This tutorial is available in two versions depending on how you would like to work with AI. Both cover the same visualization task but differ in their approach:

Choose whichever path matches your comfort level and interests. Both paths demonstrate llms.txt, markdown task files, and iterative AI collaboration.

Citations