Multimodal Models and VLMs

Multimodal models handle more than text, most commonly vision-language models (VLMs) that take images and text together. The key idea is a shared representation: a vision encoder turns an image into embeddings projected into the language model's space, so the LLM can reason over pixels and words jointly. CLIP-style contrastive training puts text and images in one embedding space, making cross-modal search possible. AI, ML, and GenAI interviews probe it because document understanding, image search, and visual agents all build on it.