Update what-is-a-vector-database.mdx

src/content/docs/vectorize/reference/what-is-a-vector-database.mdx

@@ -43,7 +43,6 @@ Instead of passing the prompt directly to the LLM, in the RAG approach you:
 1. Generate vector embeddings from an existing dataset or corpus (for example, the dataset you want to use to add additional context to the LLMs response). An existing dataset or corpus could be a product documentation, research data, technical specifications, or your product catalog and descriptions.
 2. Store the output embeddings in a Vectorize database index.
 
-When a user initiates a prompt, instead of passing it (without additional context) to the LLM, you _augment_ it with additional context:
 When a user initiates a prompt, instead of passing it (without additional context) to the LLM, you _augment_ it with additional context:
 
 1. The user prompt is passed into the same ML model used for your dataset, returning a vector embedding representation of the query.