The Core Technology: Retrieval-Augmented Generation (RAG)
Many business owners worry that AI assistants will make up fake information, also known as 'hallucinating.' This is where Retrieval-Augmented Generation (RAG) comes in. RAG is a method that restricts the AI to search only through your approved business documentation.
Instead of generating answers from general internet training, the chatbot behaves like an open-book researcher. When a customer asks a question, the RAG system performs a real-time search within your uploaded PDF files or crawled web pages to find the most relevant paragraphs first, and then uses that verified information to construct a response.
What are Vector Embeddings?
Vector embeddings are the magic behind modern semantic search. Computers do not understand words the way humans do; instead, they understand numbers.
- Text Chunking: Your documents are divided into small, manageable paragraphs (chunks).
- Vector Translation: Each chunk is passed through an embedding model, which translates the semantic meaning of the text into a mathematical vector (a long list of numbers representing coordinate points in high-dimensional space).
- Semantic Alignment: Chunks with similar meanings are stored close together in a vector database. For example, 'shipping rates' and 'delivery charges' will align close to each other.
How the Answer is Constructed
When a user asks: 'What is the refund policy for damaged shipments?', the platform converts their query into a temporary vector, performs a mathematical calculation (cosine similarity) to retrieve the closest matching text chunks from your documents, and feeds those verified chunks directly to the LLM to draft a precise response that cites specific source pages.