Core lab directory
The main chapter labs from the Prompt Cartography sequence.
Chapter 1
Chapter 1 argues that LLMs do not remove cartographic judgment. They move it into language. In this lab, you will ask a free LLM for the same map idea several ways and watch how purpose, audience, and guardrails...
Chapter 2
Build a reusable prompt packet from a casual request by applying CRAFT, separating prompt roles, and treating prompt revisions as design artifacts.
Chapter 3
Chapter 3 treats memory as something prompt cartographers design. In this lab, you will build a small knowledge document, test how retrieval changes an LLM’s map recommendations, reorganize that memory into chunks,...
Chapter 4
Chapter 4 turns prompt cartography into a workflow. In this lab, you will build a pipeline packet that names when key decisions happen, what each stage needs, and how later critique can trace output back to earlier...
Chapter 5
Chapter 5 argues that automated map generation amplifies ethical responsibility. In this lab, you will create a publishability packet for an LLM-assisted map concept: provenance, prompt metadata, attribution, bias...
Chapter 6
Chapter 6 argues that a map can work technically and still fail rhetorically, ethically, or experientially. This lab turns critique into a reproducible design dossier: students must preserve prompts, compare critique...
Chapter 7
Chapter 7 makes data engineering a core act of prompt cartography. In this lab, you will use natural language to search for open datasets, evaluate their fitness, curate a shortlist, harmonize schemas, and build a...
Chapter 8
Chapter 8 argues that you cannot prompt what you cannot name. This lab turns Dondis-style visual dimensions into a practical design language for maps. You will prompt the same map idea through contrasting forms,...
Chapter 9
Chapter 9 argues that prompt cartography does not remove design responsibility. It amplifies it. In this lab, you will expose the defaults an LLM wants to use, then replace them with a deliberate specification for...
Chapter 10
Chapter 10 reframes color as persuasion wrapped in hex codes. In this lab, you will turn color intent into a defensible palette specification that an LLM can generate, explain, critique, and revise.
Chapter 11
Chapter 11 makes typography explicit: the map has a voice, and the prompt cartographer must direct it before the model fills silence with generic web-map type.
Chapter 12
Chapter 12 moves from map voice to map marks. In this lab, you will design a complete symbol system that clarifies meaning instead of decorating location.
Chapter 13
Chapter 13 turns thematic mapping into an explicit design trial. Students learn to ask not “How should this map look?” but “What kind of representational claim is this method allowed to make?”
Chapter 14
Chapter 14 treats animation as a high-risk representational method. This lab asks students to decide when motion earns its place and how to constrain it when it does.
Chapter 15
Chapter 15 is the interaction chapter in full force: students move from “add interactivity” to directing a behavioral system with state, pacing, synchronization, restraint, and critique.
Chapter 16
Chapter 16 is synthesis. This lab asks students to revisit an earlier map idea and direct it through the full workflow until it feels coherent, restrained, publishable, and worth standing behind.
Chapter 17
Chapter 17 turns maintenance into creative stewardship. This lab gives students a durable plan for updates, drift monitoring, versioning, human QA, accessibility care, and graceful map aging.
Course reflection
The author argues that prompt cartography and AI-powered cartography should not be rejected merely because of the tools involved. Critically evaluate the disputation and consider how your view of AI and cartography has changed since beginning your course.
Prompt Cartography