How LLMs Understand Images: The Secret Behind AI That Sees

If you’ve ever asked ChatGPT to describe an image, or used Google Lens to search with a photo, you’ve witnessed something fascinating: AI doesn’t just process words anymore—it understands images too. This is a massive shift. Traditional Large Language Models (LLMs) like GPT-3 were designed only for text, but today, AI can analyze, describe, and even reason about images. So, how do machines that were originally trained to predict words now "see"? The answer lies in multimodal learning, where AI models learn to process both text and images together. Let’s break it down. The Old Way: How AI Used to Handle Images Before LLMs started looking at images, computer vision was dominated by Convolutional Neural Networks (CNNs). CNNs powered early breakthroughs in image classification, object detection, and facial recognition, but they had major limitations: ❌ They didn’t understand context—a CNN could identify a "cat," but not whether it was playing, hiding, or angry. ❌ They required huge amounts of labeled data. ❌ They were task-specific—a model trained to detect dogs couldn’t easily recognize human handwriting. Meanwhile, LLMs like GPT-3 were great at text but had zero understanding of images. The challenge was: How do we teach AI to understand both words and pictures together? Enter Vision Transformers (ViTs) and multimodal models like CLIP and GPT-4V. How LLMs Learn to "Understand" Images Before AI could process images like text, researchers had to teach LLMs how to relate words and pictures. This is done using shared embeddings, where both text and images are mapped into the same mathematical space so they can be compared. CLIP: The Model That Bridged the Gap One of the biggest breakthroughs in text-image understanding came from OpenAI’s CLIP (Contrastive Language-Image Pretraining). 1️. Two Neural Networks: One for Text, One for Images A Vision Transformer (ViT) processes images. A Text Transformer (LLM-like model) processes text. Trained Together on Image-Text Pairs Example: A picture of a "dog" is shown with the word "dog." The AI learns to associate visual and textual concepts. Shared Embedding Space CLIP projects both text and images into the same vector space. This allows AI to match captions to images or even generate text for unseen images. Feature CNNs Vision Transformers CLIP & GPT-4V Understands Context? ❌ No ✅ Yes ✅ Yes Handles Text + Images? ❌ No ❌ No ✅ Yes Can Describe Images? ❌ No ⚠️ Limited ✅ Yes Now that we know how AI links text and images, let’s dive into how Vision Transformers actually "see". How LLMs Process Image Patches Think of an image as a grid of small squares (patches), just like how text is made of words. Instead of feeding the entire image to the model, we split it into patches that act like visual tokens. Image → Patches Instead of processing an image as a whole, ViTs split it into small patches (e.g., 16x16 pixels). Each Patch Becomes a Token Just like words in a sentence, each image patch gets a vector representation. Self-Attention Looks at the Whole Image The model finds relationships between patches, understanding global context. Final Representation → Task Output The processed image can now be classified, captioned, or embedded into a multimodal model. Example: How AI Breaks an Image into Patches The AI doesn’t see one full image. Instead, it sees something like this: [Patch 1] [Patch 2] [Patch 3] [Patch 4] [Patch 5] [Patch 6] [Patch 7] [Patch 8] [Patch 9] Each patch contains a small part of the image, and the AI must piece them together using self-attention. Embedding Patches into Vectors Once the image is broken into patches, each patch is converted into a vector—a list of numbers that represent the patch’s visual information. For example, a patch might be transformed into something like this: Patch 1 → [0.23, -0.67, 1.02, 0.88, ...] Patch 2 → [0.11, -0.53, 0.95, 1.21, ...] ... These vectors act like word embeddings in NLP. They capture color, shape, texture, and spatial patterns within each patch, allowing the AI to process images similarly to how LLMs process text. Why does this matter? Just like an LLM understands that "dog" is related to "bark", a Vision Transformer can learn relationships between patches (e.g., a dog's ear connects to its face). This patch-based method lets AI process entire images in parallel, making it faster and more efficient than traditional CNNs. Thus, each patch becomes a "visual token," embedding its meaning into a space where AI can interpret it. Real-World Applications of AI That Understands Images Now that AI can see, what can it actually do? AI-Powered Search – Google Lens & Pinterest Vision let you search with pictures instead of words. Automated Image Captioning – AI generates text descriptions for images in real-time. Multimodal Assistants – AI that unde

Mar 22, 2025 - 05:06

How LLMs Understand Images: The Secret Behind AI That Sees

If you’ve ever asked ChatGPT to describe an image, or used Google Lens to search with a photo, you’ve witnessed something fascinating:

AI doesn’t just process words anymore—it understands images too.

This is a massive shift. Traditional Large Language Models (LLMs) like GPT-3 were designed only for text, but today, AI can analyze, describe, and even reason about images.

So, how do machines that were originally trained to predict words now "see"? The answer lies in multimodal learning, where AI models learn to process both text and images together.

Let’s break it down.

The Old Way: How AI Used to Handle Images

Before LLMs started looking at images, computer vision was dominated by Convolutional Neural Networks (CNNs).

CNNs powered early breakthroughs in image classification, object detection, and facial recognition, but they had major limitations:

❌ They didn’t understand context—a CNN could identify a "cat," but not whether it was playing, hiding, or angry.
❌ They required huge amounts of labeled data.
❌ They were task-specific—a model trained to detect dogs couldn’t easily recognize human handwriting.

Meanwhile, LLMs like GPT-3 were great at text but had zero understanding of images. The challenge was:

How do we teach AI to understand both words and pictures together?

Enter Vision Transformers (ViTs) and multimodal models like CLIP and GPT-4V.

How LLMs Learn to "Understand" Images

Before AI could process images like text, researchers had to teach LLMs how to relate words and pictures. This is done using shared embeddings, where both text and images are mapped into the same mathematical space so they can be compared.

CLIP: The Model That Bridged the Gap

One of the biggest breakthroughs in text-image understanding came from OpenAI’s CLIP (Contrastive Language-Image Pretraining).

1️. Two Neural Networks: One for Text, One for Images

A Vision Transformer (ViT) processes images.
A Text Transformer (LLM-like model) processes text.

Trained Together on Image-Text Pairs
- Example: A picture of a "dog" is shown with the word "dog."
- The AI learns to associate visual and textual concepts.
Shared Embedding Space
- CLIP projects both text and images into the same vector space.
- This allows AI to match captions to images or even generate text for unseen images.

Feature	CNNs	Vision Transformers	CLIP & GPT-4V
Understands Context?	❌ No	✅ Yes	✅ Yes
Handles Text + Images?	❌ No	❌ No	✅ Yes
Can Describe Images?	❌ No	⚠️ Limited	✅ Yes

Now that we know how AI links text and images, let’s dive into how Vision Transformers actually "see".

How LLMs Process Image Patches

Think of an image as a grid of small squares (patches), just like how text is made of words. Instead of feeding the entire image to the model, we split it into patches that act like visual tokens.

Image → Patches
- Instead of processing an image as a whole, ViTs split it into small patches (e.g., 16x16 pixels).
Each Patch Becomes a Token
- Just like words in a sentence, each image patch gets a vector representation.
Self-Attention Looks at the Whole Image
- The model finds relationships between patches, understanding global context.
Final Representation → Task Output
- The processed image can now be classified, captioned, or embedded into a multimodal model.

Example: How AI Breaks an Image into Patches

The AI doesn’t see one full image. Instead, it sees something like this:

[Patch 1] [Patch 2] [Patch 3]
[Patch 4] [Patch 5] [Patch 6]
[Patch 7] [Patch 8] [Patch 9]

Each patch contains a small part of the image, and the AI must piece them together using self-attention.

Embedding Patches into Vectors

Once the image is broken into patches, each patch is converted into a vector—a list of numbers that represent the patch’s visual information.

For example, a patch might be transformed into something like this:

Patch 1 → [0.23, -0.67, 1.02, 0.88, ...]
Patch 2 → [0.11, -0.53, 0.95, 1.21, ...]
...

These vectors act like word embeddings in NLP. They capture color, shape, texture, and spatial patterns within each patch, allowing the AI to process images similarly to how LLMs process text.

Why does this matter?

Just like an LLM understands that "dog" is related to "bark", a Vision Transformer can learn relationships between patches (e.g., a dog's ear connects to its face).
This patch-based method lets AI process entire images in parallel, making it faster and more efficient than traditional CNNs.

Thus, each patch becomes a "visual token," embedding its meaning into a space where AI can interpret it.

Real-World Applications of AI That Understands Images

Now that AI can see, what can it actually do?

AI-Powered Search – Google Lens & Pinterest Vision let you search with pictures instead of words.
Automated Image Captioning – AI generates text descriptions for images in real-time.
Multimodal Assistants – AI that understands both what you type and what you show it (like GPT-4V).

From e-commerce to medicine, multimodal AI is transforming how we interact with computers.