What is RAG and how we apply it at LISDATA
At LISDATA, we have a genuine commitment to research in Artificial Intelligence in general, and in language models in particular. Our R team constantly innovates in the field of Retrieval Augmented Generation (RAG), exploring all the possibilities and advances of Large Language Models (LLMs) and improving human-machine interaction through innovative solutions.
By Alberto Llera (PhD)
What RAG is and how it’s improving LLMs
RAG is a technique that combines “traditional” artificial intelligence systems for searching information in documents and subsequently integrates them with modern generative language models. At LISDATA, we use RAG to enhance the capabilities of our LLMs. By integrating retrieved information (the relevant kind) with text generation from state-of-the-art LLMs, we improve the accuracy and contextualization of the responses users receive. This allows us to offer more effective and specific interactions, avoiding the incorrect, irrelevant, or simply “made-up” responses that LLMs can produce when used directly.
Learning every day through hands-on practice
Our team has developed RAG skills through a combination of intrinsic motivation and hands-on training. We’ve taken theoretical and practical courses, covering everything from basic concepts to the latest developments in artificial intelligence. This training has enabled us to implement RAG models from scratch and adapt them to our specific needs. One of the results of this process, which we’re very proud of, is our generative artificial intelligence assistant for businesses.
Our Generative AI assistant
Our LLM-based Generative AI assistant is a virtual assistance suite that takes conventional RAG techniques a step further. In addition to improving response specificity and reducing hallucinations, it offers unique features, such as interacting with databases using natural language and the ability to cross-reference different documents to obtain related information. It can also be integrated with other solutions we’ve developed at LISDATA, such as our prediction and planning engines, giving it the ability to generate prescriptive responses. All of this makes this assistant highly useful for increasing companies’ operational efficiency and improving their competitiveness.
Working at the forefront of AI
One of the best things about working on projects like our Generative AI assistant is being at the cutting edge of LLM use. Playing an active role in advances in the field of artificial intelligence and seeing how they translate directly into practical solutions for our clients is truly rewarding. Furthermore, the high motivation of our RD team and the collaborative atmosphere within the company make this one of the most exciting projects we’ve worked on.
What we can expect from RAG in the future of business
Our team, and LISDATA as a whole, have no doubt that RAG will continue to gain relevance in the coming years, especially in the business world. The ability to interact with data and documents using natural language is incredibly valuable across a wide range of industries. That’s why, as these technologies continue to evolve, their adoption will become a necessity for companies looking to stay competitive in an increasingly digital world.
In short, we could say that RAG is transforming the way we interact with information and is pushing artificial intelligence toward new horizons. And at LISDATA, we’re committed to continuing to lead the way in this exciting area of research and development.
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