Why GPT-5 Makes “Wrong Maps” and “Gibberish Text” - And Why It’s Not the LLM’s Fault

If you’ve played with ChatGPT’s image generation features, you’ve probably seen it: coastlines that look suspiciously unfamiliar, countries with oddly shifted borders, or text in images that reads like it’s from another alphabet. Many people walk away from that experience thinking: “Wow, GPT got that completely wrong.” But here’s the twist - in most cases, GPT never drew anything in the first place.

Before I’m going to explain more in detail, let’s draft the workflow:

Internal Workflow and Tooling

The process and all required tools behind image or map creation after you submit a prompt looks roughly like this:

LLMs vs. Image Models: Two Very Different Brains

• LLM (Large Language Model) → Trained on billions of text sequences. It’s great at: - Understanding your request - Reasoning over information - Generating coherent text and instructions

• Diffusion Model (or similar image generator) → Trained on massive sets of text–image pairs. It’s great at: - Painting realistic images from prompts - Replicating visual styles But it’s not a precise drawing tool - it generates pixels from patterns, not geometric accuracy.

How Your Request Flows Through the System

When you type: “Draw me a correct map of Europe with country names” here’s what happens inside ChatGPT (simplified):

1. The LLM parses your request, understands that it needs an image, and prepares a refined image prompt.

2. The LLM sends that prompt to an image model (often a diffusion model like DALL·E or similar).

3. The image model generates pixels based on patterns it learned from its training set - not from authoritative map data.

4. The LLM sends you the image - but by then, any distortions or text errors have already happened.

Why Maps and Text Go Wrong

• No structured geometry: The image model doesn’t know about real-world coordinates - it’s matching patterns from similar images it’s seen.

• Text in images is “fake writing”: It draws letter shapes pixel-by-pixel, often producing gibberish because it’s not using a font or symbolic representation of language.

• Training data limitations: If the model saw distorted or stylized maps in training, those patterns bleed into results.

Why This Isn’t a GPT Problem

The LLM’s role is like a director telling a painter what to create. If the painter can’t draw precise political borders or perfect lettering, that’s not the director’s skill set failing - it’s the painter’s medium.

When Accuracy Matters - Use the Right Tool

If you need:

• Perfect maps → Use GIS or vector map libraries (Leaflet, Mapbox, QGIS)

• Readable text in images → Render text with a graphics library after generating the background

• Technical diagrams → Use SVG, Graphviz, Mermaid, or other structured formats

This sequence is important to understand because the inaccuracies people see - warped coastlines, misplaced borders, or unreadable text - almost always originate in step four, not in the reasoning process of the LLM itself.

Let’s look behind the curtain

GPT-5, like its predecessors, is still a Large Language Model. Its training is rooted in text: learning patterns, structure, and meaning from billions of words. When you ask it for a map or an image with text, GPT doesn’t suddenly become a drawing tool. Instead, it acts more like a project manager or a film director. It understands your request, rephrases it into a detailed image prompt, and passes that prompt on to a completely different type of AI - often a diffusion model - whose job is to actually create the image.

This is where the “wrong maps” and “AI gibberish” start to appear. Diffusion models are remarkable at turning a text description into a plausible-looking picture, but they are not grounded in precise data. They don’t have coordinates, political borders, or real-world measurements stored in neat, structured formats. They’ve learned to draw based on patterns in their training data, which might include a lot of stylized, simplified, or even inaccurate maps. So when you ask for “a perfectly accurate map of Europe,” what you’re really getting is “an image that looks like maps of Europe the model has seen before.” That’s a big difference.

The same problem happens with text inside images. Diffusion models don’t “write” text the way your computer types it with a font. They paint shapes of letters, learned from thousands of examples, and try to approximate the look of real words. Because they’re essentially guessing the letterforms pixel by pixel, it’s no surprise that the results often look like scrambled alphabets.

A brief digression into diffusion models

A diffusion model is a type of generative AI trained to create images from text descriptions, and it works very differently from a language model. Instead of predicting the next word in a sentence, it learns to gradually turn random noise into a coherent picture. Think of it like sculpting from a block of marble - only here, the “block” starts as a cloud of digital static, and with each step, the model refines the noise into shapes, colors, and textures that match the prompt it was given.

The model learns this skill during training by repeatedly corrupting real images with noise and then practicing how to reverse the process. Over time, it becomes adept at recognizing patterns that “look like” a cat, a coastline, or a letter, even though it has no understanding of what those things mean in the real world.

If you prefer a simpler analogy: imagine asking a painter who has seen millions of pictures to recreate one from memory using only fuzzy, low-quality sketches as a starting point. The result may look right at first glance, but it’s easy for small details - like the exact shape of a border or the spelling of a word - to be off.

This is the core reason maps can appear distorted and text in images can turn into nonsense. The diffusion model excels at producing plausible visuals, but it lacks symbolic precision and has no awareness of what it is actually depicting.

GPT-5 producing wrong maps with wrong labelsGPT-5 producing wrong maps with wrong labelsGPT-5 producing wrong maps with wrong labels

Back to GPT

Understanding this, it becomes clear: this isn’t a failure of GPT’s reasoning ability - it’s simply a matter of passing the baton to a teammate with different skills. The LLM can understand and describe your idea with precision, but once that idea is handed to a model that paints pixels instead of thinking in structured data, some accuracy is inevitably lost.

If you truly need perfection - a 100% accurate political map, readable text on a poster, or a technical diagram - you can’t rely solely on an image generator. You’ll want to bring in tools that work with structured formats: GIS systems like QGIS or Mapbox for maps, graphics libraries for crisp text rendering, or diagramming tools like Graphviz and Mermaid for schematics.

In other words, GPT-5 is the conductor, not the violinist. It directs the flow, keeps everything in sync, and knows what each instrument should play - but if one section of the orchestra can’t hit the note, it’s not because the conductor forgot how music works. It’s because the instrument itself isn’t built for that level of precision.

Conclusion

GPT-5 is still a Large Language Model. The “wrong maps” and “AI gibberish” in images come from the limitations of the image generation component, not from the LLM’s text reasoning. The real magic happens when you combine both: let the LLM orchestrate, but give it precise tools for the final output.