What if your company data was not just stored, but could actually be used in an "understanding" way? What if data didn't just rest in an archive, but could be used dynamically to find answers to your own questions at any time? The combination of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) leads to exactly that: a source of knowledge that is available intelligently and context-sensitively on demand.
This new tandem not only gives companies faster access to internal knowledge, but also deeper insights and customised solutions in real time. In the following, we look at how LLMs and RAG can give companies a real knowledge advantage. An overview from René Kessler's technical article in Industry of things.
The digital transformation has already provided companies with many innovative tools. Today, digital assistants based on LLMs such as OpenAI's GPT-4 enable highly precise, contextualised support in day-to-day work. They reduce research times and automate routine activities, allowing employees to concentrate on more demanding tasks.
However, LLMs often reach their limits as they are only based on general knowledge that is already known. This is where the Retrieval Augmented Generation comes into play, which removes this limit by accessing specific internal company data.
RAG combines the text generation capabilities of LLMs with the ability to retrieve relevant information from internal databases. The main advantage? The result is a kind of "super assistant" that delivers specific, contextualised answers from the company's own secure data sources. RAG combines powerful language models with semantic vector technology and vector databases to make relevant internal information available efficiently.
Put simply, the RAG process converts a query into an embedding, searches the vector database for the most relevant information and passes the results to the LLM, which generates a precise, contextualised response.
By combining internal data sources with intelligent language processing, RAG systems provide companies with precise answers - without long implementation times.
They also simplify the data-based creation of reports and analyses by efficiently combining and clearly presenting information from different sources.
The combination of RAG and LLMs takes knowledge management to a new level: companies benefit from fast access to internal data, flexible customisability and a significant increase in efficiency. Curious to find out how RAGs and LLMs can make a difference to your day-to-day business? For more in-depth insights and practical use cases of LLMs and RAGs, read the technical article in Industry of Things.
