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Core
Multimodal Models and VLMs
Multimodal models process 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, enabling cross-modal search. Applied-AI interviews probe it because document understanding, image search, and visual agents all build on it.
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System Design for AI in ProductionDesign a multimodal (text and image) search system for a large e-commerce catalog.→System Design for AI in ProductionDesign a visual / image search system (search by image, or text-to-image search).→Machine Learning & Data ScienceHow do vision-language models (VLMs) work, and how does CLIP enable cross-modal understanding?→RAG & Agent System DesignWhat is multimodal RAG, and how does it differ from text-only RAG?→System Design for AI in ProductionDesign a system to retrieve similar scenes from a large video corpus given a query clip.→LLM & GenAI FundamentalsHow does a vision-language model connect an image encoder to an LLM, and where does it fail?→
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