I think LLM inference is extremely similar to search. One of the killer features of LLMs is summarization (which can be thought of as searching a noisy set of

I think LLM inference is extremely similar to search. One of the killer features of LLMs is summarization (which can be thought of as searching a noisy set of data for the most relevant information) and document QA, which is also a search function. Even implementation wise, transformers encode information in their weights and include that relevant information in their response. Image generation models work the same way as google image search. Key word soup in -> relevant image out. They encode information seen in their training within their weights then filter down those weight over many layers and what’s leftover is relevant data. Idk how you’d look at how these models work and what they do and not see search.