diff --git a/docs/docs_skeleton/docs/use_cases/question_answering/how_to/chat_vector_db.mdx b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/chat_vector_db.mdx
index 906d576c51..56f9dda39c 100644
--- a/docs/docs_skeleton/docs/use_cases/question_answering/how_to/chat_vector_db.mdx
+++ b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/chat_vector_db.mdx
@@ -5,7 +5,7 @@ sidebar_position: 2
 # Store and reference chat history
 The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.
 
-It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question answering chain to return a response.
+It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question-answering chain to return a response.
 
 To create one, you will need a retriever. In the below example, we will create one from a vector store, which can be created from embeddings.