Fine-Tuning

Fine-tuning adapts a general model to a narrower task or domain by continuing its training on curated examples. It changes the model's learned parameters, whereas retrieval-augmented approaches inject fresh information at query time without retraining. Both shape what a model produces, but through different mechanisms.

For most brands, AI visibility is driven far more by retrieval and by the public information models learn during broad training than by any bespoke fine-tuning. Understanding the distinction helps clarify why AEO focuses on being retrievable and consistently represented across the web rather than on trying to alter a model directly.

DeepLexa's approach targets the levers brands can actually pull, entity signals, retrievable content, and authoritative mentions, rather than the model internals fine-tuning adjusts.