[potatoman22]

Kind of. You feed the LLM the input text for your prediction, you extract the activations of the final layer of the LLM (so the weights * the input of the previous layers), then use that activation vector, or embedding, as the input for a separate model. This separate model that uses the embedding can be any classifier or regression. A common use case for this is document classification.