Why is RAG the only safe way to use AI in a company?
In short (BLUF):
Implementing publicly available ChatGPT in a company carries the risk of leaking sensitive operational data and susceptibility to so-called "hallucinations" (AI making up facts). The solution is RAG (Retrieval-Augmented Generation) – a closed system with a vector database, in which the artificial intelligence model (LLM) answers exclusively based on selected company documents you have given it access to.
The problem with classic LLM models (e.g. ChatGPT)
Most companies at the beginning of their journey with artificial intelligence decide to buy a subscription to ChatGPT or a similar tool for their employees. Although this brings initial gains in productivity, three critical problems quickly arise:
- Data leak: Everything you paste into publicly available models can be used to train them. Your source codes, financial strategies, and customer data cease to be confidential.
- Lack of domain knowledge: ChatGPT does not know the procedures in your company, does not know what your prices are, what internal recruitment processes look like, and what your technical standards are.
- Hallucinations: Forced to answer something it has no idea about, the LLM model will simply make it up and will defend this fabrication with absolute certainty.
RAG technology as the B2B gold standard
RAG consists of combining the huge linguistic capabilities of a model (understanding intentions, fluent grammar, generating conclusions) with a safe, closed knowledge resource (vector database).
When an employee (or customer) asks a chatbot a question in the RAG architecture, the following thing happens:
- The question is converted into a so-called "vector", which searches your internal documents, PDFs, emails, and company wiki (e.g., Confluence).
- The system extracts perfectly matching fragments of regulations or instructions.
- This fragment is sent to the LLM model (in a secure, hermetic API environment), along with the order: "Answer this question ONLY and exclusively based on the following text".
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