What is Memory Augmentation?
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.
Frequently Asked Questions
How does memory augmentation benefit AI models?
It allows models to retain and leverage historical context, improving performance in tasks like context-aware generation.
What technologies are used in memory augmentation?
Technologies include retrieval augmentation pipelines and external knowledge bases.
From the blog

Custom model training and fine-tuning for GPT-3.5 Turbo
Today OpenAI announced that businesses and developers can now fine-tune GPT-3.5 Turbo using their own data. Find out how you can create a custom tuned model trained on your own data.

Herman Schutte
Founder

Create an AI version of yourself for your coaching business
Harnessing the power of Artificial Intelligence is no longer reserved for tech giants or sci-fi enthusiasts. As a coach, what if you could scale your expertise, offering guidance at any hour without extending your workday?

Herman Schutte
Founder