Beginner LevelAI, LLMs, Embeddings & MCP
What are Multimodal LLMs (Gemini Vision, GPT-4o) and how do they process images and audio?
❓ Interview Prompt: Explain vision encoders, patch projection tokens, audio waveforms, and native multimodal attention.
Multimodal Large Language Models can process and generate multiple modalities of data (text, images, audio, video) within a single unified architecture.
How Vision Processing Works:
1
Images are split into smaller square visual patches (e.g. 16x16 pixels).
2
A Vision Transformer (ViT) encoder maps patches into visual feature vectors.
3
A linear projection layer maps visual vectors into the same embedding space as text tokens.
4
The main Transformer processes image tokens and text tokens together seamlessly.
Code Implementation Example
typescript
// Multi-modal API call sending image buffer alongside text prompt
const response = await aiClient.generateContent({
model: 'gemini-1.5-flash',
contents: [
{ inlineData: { mimeType: 'image/png', data: base64ImageBuffer } },
{ text: 'Extract all table row items from this receipt image as JSON.' }
]
});💡Passing base64 image data alongside text prompt in multimodal API call.
🎯 Interview Key Takeaway
Multimodal LLMs convert visual patches and audio frames into embedding tokens within a shared Transformer space.
#Multimodal#Vision#Gemini#GPT-4o#Beginner
Back to all AI, LLMs, Embeddings & MCP Questions→