A method where external memory modules or databases are integrated with AI systems to enhance their knowledge and context retention.
More about Memory Augmentation
Memory Augmentation integrates external memory modules, such as vector databases or specialized data structures, with AI systems to improve their ability to retain and utilize context over time. This approach is especially valuable in tasks requiring persistent context, such as multi-turn dialogue with retrieval and knowledge-grounded generation.
By augmenting memory, AI systems can reference past interactions or historical data, enabling more coherent and contextually aware responses.