Example Concept: In a RAG model, the process begins by converting your custom knowledge base documents into embeddings using a pre-trained model. These embeddings are stored in a vector database. During inference, the input query is also converted into an embedding, which is then used to search the vector database for the most relevant documents. The retrieved documents are combined with the query to generate a more informed response.