Overview of Deep Lake tools for increasing retrieval accuracy
How to Use Deep Memory to Improve Retrieval Accuracy in Your LLM Apps
Deep Memory is a suite of tools that enables you to optimize your Vector Store for your use-case and achieve higher accuracy in your LLM apps.
Deep Memory computes a transformation that converts your embeddings into an embedding space that is tailored for your use case. This increases the accuracy of your Vector Search by up to 22%, which significantly impacts the user experience of your LLM applications.
Furthermore, Deep Memory can also be used to decrease costs by reducing the amount of context (k) that must be injected into the LLM prompt to achieve a given accuracy, thereby reducing token usage.