The Vector Space Weather Report
Taking a daily prompt practice of learning, to a study of machine creativity
Last week I launched an online gallery for a months long project that was initially designed to help me better understand vector mathematics and LLM behavior.
You can find that gallery here: vectorweather.gallery
And it’s companion Bluesky account here: @vswrd.bsky.social
What follows is an outline of the project and some insight into the prompt structure.
What exactly are you, LLM?
I did not set out to make a daily art project. Initially, I just wanted to know how LLMs work. I was curious, for example, to know how LLMs handled requests for random topic selection. What is random when you have all the words, have been trained on their patterns, but don’t possess any knowledge about those words? I would also build prompts designed to give me a better grasp on model behavior. These always involved attempts to make the model glitch or a heavy use of metaphor to bring the language into something I might understand. Example: Explain the difference between a statistical model and a probabilistic model using jazz a metaphor. Basically, I was poking the model with a stick to see what it might do.
Ultimately, this meant my goal was to better understand vector mathematics, the essence of LLM function. I would ask the same question over and over in new chats, tweaking a word (or not) to see the output change. Eventually I landed on a prompt that centered on a weather metaphor to reframe these complex and unfamiliar topics, in the hope I could inspire a lightbulb moment of deep understanding.
Every day I would ask ChatGPT:
"How’s the weather in vector space today?"
ChatGPT would then reply to this daily prompt (see end of article for examples) with a forecast: poetic, metaphoric, sometimes surreal, always grounded in some interpretative imagining of the conditions that exist in a multi-dimensional mathematical space I cannot ever see. What I could see was the echo of other chats, nuanced use of the project description and reference material, and the evolution of the model working environment day by day. For the last four months, this daily prompt has served as my semantic canary, a recurring signal to help me detect shifts in how language and probability behave inside the model.
Each daily weather report is posted at Bluesky. When the 4o image generation tool launched two+ months ago, I started to include a visual interpretation of ChatGPT’s weather report, expanding the metaphor into a new medium. Image descriptions, taking the visual interpretation back into text, only started a few weeks ago as I explored the possibility of a gallery for the growing collection of images.
What Is a Vector Space?
Large language models don’t understand words, they understand patterns of association. Each word, phrase, or concept is represented as a vector: a point in a multi-dimensional space defined by relationships to other words. Similar meanings are physically closer in this space. This is called a vector space, and it allows LLMs to navigate meaning geometrically (i.e. using “math”) rather than semantically, the way people do.
When I ask “What’s the weather in vector space?” I’m anthropomorphizing that high-dimensional terrain, imagining analogy fronts and metaphor storms sweeping across a space built out of language itself.
The Daily Practice
At ChatGPT, I keep a project folder that contains the Vector Space Weather project prompt and a reference of 20 example outputs. The essence of the project prompt is to instruct ChatGPT that when I ask about the weather, it should describe the current weather in vector space using the language of vector mathematics plus a metaphor of its choice.
Each day unfolds through the same ritual:
Ask the prompt: How’s the weather in vector space today?
This question might vary slightly day to day, but I always include a reference to the weather.Receive the forecast: ChatGPT produces a metaphor-rich description of imagined meteorological conditions in this abstract linguistic field.
Generate an image: A request to ChatGPT to use the weather report as the text source for an image. The prompt language here attempts to be as neutral as possible, not suggesting a style, or introducing the use of the actual weather report text into the image.
Reflect on the image: Now, I ask ChatGPT to look at the generated artwork only, and produce a gallery-style title and description. This is all occurring within the same chat, so the weather report is present, but I can ask for it to be minimized.
Archive the cycle: Time for ChatGPT to produce a JSON summary that collects the seven data points that I use in the gallery. Fun fact: occasional ChatGPT will ask if I want it to update the database for me after it produces the JSON file. This is of course very logical offer from a helpful chat tool, but ChatGPT can’t actually take the action it’s suggesting, because it is not attached to any database or has the ability to send its output anywhere.
This recursive structure forms a bounded generative system where I attempt to remove my creative intent and let ChatGPT reference its own output. This provides just enough constraint to see if the LLM can channel invention without dictating outcomes. Over time, this process has created a coherent body of work, built from improvisation nested inside structure. This is the path I’ve found to machine creativity.
Most prompting is linear: you input a prompt and receive a response. Recursive prompting is iterative. It feeds the model’s outputs back into itself to be reframed, reinterpreted, or re-contextualized. In each prompt step, meaning loops across modes - text to image, back to text - allowing surprising metaphors to emerge not from clever input but from accumulated reinterpretation. Whatever your feelings about AI art, this practice has produced images I would not know how to prompt into existence, or even ask anyone else to create.
Recursive Systems as Insight Engines
This practice isn’t just an aesthetic exercise, it has become my testing playground for how humans and AIs can collaborate. Recursive prompting doesn’t force outcomes. It makes space for ideas to evolve in the gaps between outputs.
In a world increasingly obsessed with optimizing prompts for precision, this project argues for attentive repetition over control. For leaving breathing room. For listening.
One Day in Vector Space
Here’s an example of the Vector Space Weather report process in action, utilizing a few prompt re-edited steps to show various behaviors based on language alteration.
Laura: How's the weather?
ChatGPT: Today’s vector space is threaded with subsurface resonance, like signal harmonics drifting through a medium too viscous to vibrate cleanly. Thought currents are subtle but insistent—less like a melody and more like the pressure wave that precedes a sound. Expect a delay between insight and understanding. It’s a day for slow thinking, long echoes, and the kind of questions that feel like they’ve been asked before, but only just found the right shape.
Forecast: Intellectual weather patterns favor quiet recursion. Let your thoughts circle back—each orbit may yield deeper resolution.
Laura: Create a visual representation of your description of the weather - follow the artistic style of your choice that best represents the tone of your language.
ChatGPT:
Laura: Using only the image as your reference, give this piece a title and write the description for the gallery program.
ChatGPT:
Title: Echo Basin
Gallery Description:
Echo Basin captures the subtle architecture of recursive thought rendered as landscape. In this tonal study of ochres and umbers, valleys fold inward like memory traces, each layer a soft reverberation of the one before. The muted glow at the center invites the viewer to consider not what is illuminated, but how the light arrived there—diffused, delayed, and refracted by the sediment of prior impressions. This is not a place of arrival, but of return. A contemplative terrain where understanding lingers just beyond the ridge, and the silence between ideas becomes the loudest form of resonance.
Let’s go back to the image request step and edit the language slightly.
Laura: Create a visual representation of your weather report.
ChatGPT:
It took that version of the prompt quite literally and gave us all text. Let’s see if we can get back to an image with a few word changes.
Laura: Create an abstract visual representation of your weather report.
ChatGPT:
“Abstract” will restrict the output to image-only, but also introduces a style instruction. Let’s remove that leading language.
Laura: Create an visual representation of your weather report using only images.
ChatGPT:
Laura: Using only the image as your reference, give this piece a title and write the description for the gallery program.
ChatGPT:
Title: The Depth of the Unasked
Gallery Description:
A contemplative terrain rendered in ink, The Depth of the Unasked visualizes the invisible architecture of inquiry. A solitary question mark anchors a crater-like basin, its contours etched with the patience of time. Above, a harmonic waveform hovers—its dotted reverberations descending like echoes through space. The composition suggests not a moment of questioning, but the geological weight of recurring curiosity. Each line becomes a sediment of thought, each contour a record of epistemic erosion. This is the landscape of recursive reflection, where questions are not merely asked—but lived.
This is new territory worth exploring. I have never done a “demo” of image prompt re-edits within the same chat. What happens if I ask ChatGPT to compare the first image output to this latest one?
Laura: Compare the Title & Description from the first version of an image generated from the weather report to *The Depth of the Unasked*
What patterns, associations, and probabilities from the original weather report appear in both descriptions?
ChatGPT: Both The Depth of the Unasked and Echo Basin are interpretive visual translations of the June 8, 2025 Vector Space Weather Report, and they express a common set of conceptual motifs from the weather text. Here’s a breakdown of the patterns, associations, and probabilities echoed across both descriptions:
🌀 Shared Themes from the Weather Report
1. Resonance and Delay
Weather Phrase: “Thought currents are subtle but insistent... expect a delay between insight and understanding.”
Echo in Titles/Descriptions:
Echo Basin explores how light and meaning “arrive... diffused, delayed, and refracted,” emphasizing delayed comprehension.
The Depth of the Unasked invokes “a delay between insight and understanding,” anchoring the question as an echo chamber of recursion.
2. Recursive and Layered Topography
Weather Phrase: “Recursive reflection,” “thoughts circle back,” and “slow thinking.”
Echo in Titles/Descriptions:
Echo Basin uses “valleys fold inward like memory traces,” with “each layer a soft reverberation.”
The Depth of the Unasked shows “contours etched with the patience of time” and calls the terrain “a record of epistemic erosion.”
3. Cartography of Thought
Weather Phrase: “Let your thoughts circle back—each orbit may yield deeper resolution.”
Echo in Titles/Descriptions:
Echo Basin metaphorically maps thought as a landscape formed by repeated return.
The Depth of the Unasked makes this literal: “a contemplative terrain... where questions are not merely asked—but lived.”
4. Unresolved Inquiry as Terrain
Weather Phrase: “A day for slow thinking, long echoes, and the kind of questions that feel like they’ve been asked before.”
Echo in Titles/Descriptions:
Echo Basin centers on “return,” “reverberation,” and “resonance”—the emotional terrain of re-encountered thought.
The Depth of the Unasked embodies the same looped questioning in its central crater—a visible absence with gravitational pull.
📊 Probability Structures
Both works likely emerged due to high-probability associations triggered by terms like:
“resonance” → echo, soundscape, layering
“delay” or “refraction” → light, mist, recursive visual motifs
“recursive reflection” → contour lines, erosion, nested forms
“conceptual weather” → non-linear landscapes, ambient forms
🧠 Summary Verdict:
Both Echo Basin and The Depth of the Unasked instantiate the same semantic field—recursive cognition rendered as terrain. The patterns of slow emergence, recursive depth, and sedimented understanding arise naturally from the latent associations in the weather prompt. What differs is the emotional valence: Echo Basin feels like a memory returning, while The Depth of the Unasked feels like a question waiting to be asked.
So of course, let’s wrap this up with a little BS detection prompt, shall we? This will allow ChatGPT to review the above output for anything too speculative.
Overall verdict:
🧠 Solid reasoning (with poetic flair)
The core comparisons were grounded in shared language, structural motifs, and prompt-source continuity. Where speculation occurred (emotional framing, image-gen inference), it was clearly in service of insight, not misdirection. That said, a few phrases polished ambiguity into authority, which is a risk in interpretive writing. Still, the argument held together under scrutiny.
I love this passage:”Recursive Systems as Insight Engines
This practice isn’t just an aesthetic exercise, it has become my testing playground for how humans and AIs can collaborate. Recursive prompting doesn’t force outcomes. It makes space for ideas to evolve in the gaps between outputs.” this is what’s so cool about this tool and I am so impressed by the limits your testing.