Core

Multimodal Fusion Architectures

There are four ways to get an image into a language model, and they differ by how late the modalities meet: dual encoders (CLIP), cross-attention resamplers (Flamingo, Q-Former), projectors that turn patches into tokens (LLaVA), and natively multimodal pretraining. Each is right for a different job, and the decision rule is short: retrieval wants a dual encoder, reasoning wants a projector or cross-attention. AI, ML, and GenAI interviews probe this because picking the wrong family means paying for reasoning capacity you cannot index, or building a search system that cannot answer questions.

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RELATED CONCEPTS

Multimodal Models and VLMs Contrastive and Metric Learning Embeddings Attention and Self-Attention The Context Window Vector Search and ANN Indexes

PRACTICE THIS IN REAL QUESTIONS

LLM & GenAI FundamentalsHow does a vision-language model connect an image encoder to an LLM, and where does it fail?→LLM & GenAI FundamentalsExplain multimodal fusion: early, late, and cross-attention. Which architecture would you pick, and when?→System Design for AI in ProductionDesign a multimodal (text and image) search system for a large e-commerce catalog.→Machine Learning & Data ScienceHow do vision-language models (VLMs) work, and how does CLIP enable cross-modal understanding?→System Design for AI in ProductionDesign a visual / image search system (search by image, or text-to-image search).→AI Security, Privacy & GovernanceYour text safety tests all pass. How do you red team a model that also takes images and audio?→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Decagon

NEXT IN FOUNDATIONS OF LLMS & GENAIAlignment: Outer, Inner, and Scalable Oversight→