The data stored in vector databases is the key to the success of the generative AI (GENAI) for companies in all sectors.
The data stored in vector databases is the key to the success of the generative AI (GENAI) for companies in all sectors. Private and updated data from company data sources, including unstructured data and structured data, is necessary when IA inference to make Genai models more precise and more relevant.
In order for the data to remain useful for the GENAI after initial training, RAG architecture (Retrieval-Augmented Generation) is the frame of reference.
The RAG strengthens AI models by using the relevant and exclusive data of business databases to improve precision. An effective RAG deployment includes all the data selected to maintain the up -to -date AI process.
Concretely, the RAG allows companies to automatically generate more specific answers to customer or employee issues. It allows AI models, including LLM as a chatgpt, to reference information exceeding their initial training data, accessing proprietary data stored in the corporate infrastructure.
Left to themselves, the LLM and SLM are either static, or exploit only information accessible to the public, such as the information available on the Internet. These natural language applications, data -oriented and used to answer questions from users, must be able to meet sources of information authority throughout the company. This dynamic has placed business storage at the Center for the adoption of the GENAI in corporate environments through RAG architecture.
Requirements relating to the storage infrastructure with Genai
The storage infrastructure must be cybersecated and available at 100 %. No stop time! No data compromise! It must be flexible, profitable and capable of operating in a multi-cloud hybrid environment, which is increasingly the standard environment of large companies today.
You must also look for a storage system that offers the lowest possible latency. Believe me, you want your storage infrastructure to be very efficient and ultra-by when you have launched your AI project and you will go into production mode. In addition, it is essential to have a RAG configuration allowing you to obtain all the data sources you need through several suppliers, as well as your data in your hybrid multi-cloud environment, in order to obtain a precise AI.
Having a business storage system with a workload Rag deployment architecture – and the appropriate capacities for AI deployments – will give you, to you and your organization, the certainty that your IT infrastructure is capable of operating large sets of data and quickly extracting relevant information. The vector databases used in company storage systems optimized by RAG extract data from all selected data sources and provide simple and effective means of looking for them and drawing lessons.
It has been said that the way AI learns is semantic learning. It is essentially a question of increasing knowledge on the basis of previous knowledge. The AI model has its “brain” which has been formed on gigantic quantities of information accessible to the public – training at AI, generally done in a hyperscular environment – but when it arrives in the company, you must obtain this data from your corporate data sources, so that AI can be updated and personalized – IA inference. Thus, the AI model can give meaning not only to words, but also to the appropriate context. During the IA inference phase, the AI model applies the knowledge it has acquired. You don’t want your AI to have hallucinations, right?
The evolution of the company’s storage infrastructure cannot be ignored in this situation either. It is certain that the typical company will not have the capacity or the means of carrying out the initial training of an LLM or an SLM independently, as do hyperscalers. The formation of an LLM requires a robust and highly scalable computer system.
However, the interconnection between a hyperscaler and a company – a transparent transfer which is necessary for the GENAI to become more useful to companies in the real world – requires that companies have data storage on the level of the petact, of professional quality. Even medium -sized companies must consider storage at the petactical scale to adapt to the rapid changes of the AI.
The data value increases when you transform your static support infrastructure into a new generation dynamic and super-intelligent platform to accelerate and improve the digital transformation of AI.
