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Multimodal LLMs — CLIP, Vision-Language Models & Production Vision APIs
How image+text models work at scale — contrastive pretraining, projection layers, and LLaVA-style instruction tuning. Covers evaluation (MMMU, VQA, retrieval), latency and token economics, and failure modes interviewers expect you to name (hallucinated objects, OCR brittleness, eval contamination).
Why Multimodal LLMs Are a Different Product Problem
A vision-language model (VLM) is not "an LLM with images pasted in." At inference time you juggle three budgets: (1) vision compute (patch embeddings through a ViT or ConvNeXT backbone), (2) language compute (the same autoregressive transformer stack you already know), and (3) context tokens where each image can consume hundreds or thousands of image tokens before the user ever types a character.
Interviewers in 2025–2026 care less about name-dropping CLIP and more about systems: How do you evaluate a VLM for your SKU-matching or medical-imaging use case? When do you use multimodal RAG (retrieve images+text) vs single-image in-context reasoning? How do you catch object hallucination and OCR confusions at scale? This topic ties the five GenAI planes together — retrieval, generation, evaluation, reliability, operations — using multimodal language as the running example.
What interviewers are really testing
Mid level: can describe CLIP as contrastive image-text training; names GPT-4V / Gemini as multimodal products.
Senior level: explains ViT → projector → LLM interface; names concrete benchmarks; discusses cost per image and P95 latency.
Staff level: specifies evaluation design for domain data (not just MMMU), multimodal RAG tradeoffs, safety around sensitive images, and how production monitoring differs from unimodal LLM serving.
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