Core

Multimodal Models and VLMs

A vision-language model lets an LLM see by running images through a vision encoder and a projection layer that turns them into tokens the language model reads alongside text. CLIP-style contrastive training aligns image and text into one embedding space, powering image search and zero-shot classification. FDE loops probe this because document, chart, and screenshot understanding is a common deployment, and the failure modes (counting, fine detail, hallucinated visual facts) are specific.

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RELATED CONCEPTS

Embeddings & Vector Representations Retrieval-Augmented Generation (RAG)Tokenization & Tokens

PRACTICE THIS IN REAL QUESTIONS

Machine Learning & Data ScienceContrastive loss vs triplet loss for training embedding models: when would you reach for each?→System Design & Production EngineeringDesign a real-time radiology copilot that flags regions and drafts a report in under 2 seconds at 10 studies/sec, recall first.→ML Infrastructure & GPUsRun a 7B vision-language model on a Jetson Orin to caption a 25 FPS video stream within 500ms.→RAG & Agent System DesignBuild multimodal RAG over 10,000 PowerPoint decks that answers chart queries and cites the exact slide.→LLM & GenAI FundamentalsYour voice agent talks over callers and mishears account numbers, yet word error rate is 6%. Diagnose both, and say what to measure instead.→LLM & GenAI FundamentalsDesign the eval for a voice agent taking inbound support calls. What do you measure offline, and what can only production tell you?→