Retrieval-Augmented Generation

Retrieval-Augmented Generation. More and more entries are dedicated to AI solutions. Is this just an AI fashion trend or a lasting impact on the development of our technologies. It seems that AI solutions are yet to experience their true development. This can be seen at every turn and in the financial outlays that commercial companies are dedicating to the development of such AI-based solutions. Microsoft and IBM, among others, are also active in this matter. See the article from Microsoft.

Retrieval-Augmented Generation (RAG) is an AI framework developed by IBM to enhance large language models (LLMs). RAG combines the generative power of LLMs with external knowledge bases for accurate, current information. It tackles LLMs' occasional inaccuracy by grounding responses in external data, reducing misinformation risks and training needs. RAG's application in enterprise settings promises computational and financial efficiency for chatbots. It represents an 'open book' approach to AI, offering personalized, verifiable responses. For a deeper dive into this topic, read the full article on [IBM's blog].

It's not clear exactly where it's all going and what challenges lie ahead. These types of solutions will be helpful in the search for information to fill in the holes in the theories that have so far needed refinement. It may also be interesting to note that AI-based solutions such as ChatGPT can also analyze the consistency of information that has so far not been able to be linked together, as it is most often metadata from different areas of knowledge and science. This is why, among other reasons, so much development time is dedicated by commercial companies to such solutions as Retrieval-Augmented Generation. Maybe this is an opportunity for the Quantum Space Concept.

Marek Ożarowski