The concept of Retrieval-Augmented Generation (RAG) has its roots in the early 2000s, when researchers began exploring ways to combine retrieval and generation techniques in natural language processing (NLP).
However, the modern version of RAG, which integrates retrieval with large language models, emerged around 2019-2020.
This was largely driven by the development of transformer-based architectures, such as BERT and RoBERTa, which demonstrated the potential of retrieval-augmented approaches for improving language understanding and generation tasks.
Since then, RAG has gained significant attention and has been applied to various NLP tasks, including question answering, text summarization, and dialogue generation.
The RAG framework has undergone significant improvements, with advancements in retrieval algorithms, indexing techniques, and integration with large language models.
Today, RAG is considered a key component of many state-of-the-art NLP systems, enabling more accurate and informative responses in a wide range of applications.
Would you like to know more about the current state of RAG research or its potential applications in real-world scenarios?
Is there the potential to implement RAG with blockchain or are they incompatible?
Elaborate on this...what are the use cases and applications? Go into more detail.
Would DAG implementation be built in the blockchains code base or is this a second layer that could be built?
Where would a RAG system with blockchain reside? Would that be at the base code or a layer 2?