Embedding is a transformation which allows us to find semantically relevant chunks from a catalogue given a query. Through some nearness criteria, you would retrieve "semantically relevant" chunks which along with query would be fed to LLMs and ask them to synthesize the best answer. Vespa docs are very great if you are thinking of building in this space. Retrieval part is independent of synthesis, hence it has its separate leaderboard on huggingface.
https://docs.vespa.ai/en/embedding.html
https://huggingface.co/spaces/mteb/leaderboard