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VLMs like GPT-4V and LLaVA bolt a vision encoder onto a language model through a projector. The signal is the image-tokens-as-prefix design, the alignment training, and the resolution and hallucination failure modes.

Multimodal is now table stakes, and this checks whether you understand how images and text reach a shared model. The signal is CLIP's contrastive alignment and how modern VLMs feed image features into an LLM's token space.

Multimodal search tests whether you understand shared embedding spaces and the retrieve-then-rank pattern under a real catalog's scale and freshness. The signal is CLIP-style joint embeddings plus hybrid retrieval and honest relevance evaluation. Here is the design.

Image search is embedding retrieval at billion-image scale. The tell of a strong answer is knowing when a vision-only encoder suffices and when you need a CLIP-style shared space, plus how the ANN index actually serves the query. Here is the design.