RAG vs. standard AI: A standard language model can only answer based on what it was trained on. RAG connects the model to your data in real time, so the answer is always up to date, relevant and verifiable.
What is RAG?
RAG stands for Retrieval-Augmented Generation. Instead of generating answers from general knowledge, the assistant first searches your knowledge base, your documents and your systems. It then formulates a response based on what it actually found. A standard language model can only answer based on what it was trained on. RAG connects the model to your data in real time, so the answer is always up to date, relevant and verifiable.
Why it matters
Common AI assistants have a fundamental problem: they make things up. When they don't know the answer, they invent something that sounds right. That creates frustration and mistrust among your customers. With RAG, KonversAI only answers based on what it actually knows. If it doesn't know the answer, it says so honestly and escalates to a human with full context.
Your knowledge base, your answers
KonversAI builds its answers on content you control: opening hours, return policies, product information, price lists, FAQ and internal procedures. You decide what the assistant knows, and you can update it at any time. No technical expertise required.
Use cases
RAG in practice
E-commerce
The customer asks about delivery time for a specific order. The assistant retrieves real-time data from the order system and responds precisely, without guessing.
Sports and associations
A member asks about licence requirements for next season. The assistant retrieves current regulations from the knowledge base and responds correctly.
Travel
A guest asks what is included in the package deal. The assistant retrieves current prices and terms and responds without mixing in outdated information.
Health
A patient asks what they need to remember before an appointment. The assistant retrieves relevant preparation information from the clinic system.
Integrations
Works with the systems you already use
FAQ
Frequently asked questions
What happens if the assistant can't find the answer in the knowledge base?
It honestly says it doesn't know and escalates to a human with full context from the conversation.
Do we need to build the knowledge base from scratch?
No. We index existing content from your website and documents you already have. We also help clean up and quality-assure the content.
How often is the knowledge base updated?
You can update content at any time. Changes are reflected immediately in the assistant's answers.
Is RAG the same as a regular chatbot?
No. A regular chatbot follows predefined rules and answers. RAG uses a language model combined with real-time search of your data, giving far more precise and flexible answers.
What is the difference between RAG and fine-tuning?
Fine-tuning retrains the model on your data, which is expensive and time-consuming. RAG connects the model to your data in real time without modifying the model itself.