diff --git a/langchain/chains/vector_db_qa/base.py b/langchain/chains/vector_db_qa/base.py
index d54de11ca..e0e158c75 100644
--- a/langchain/chains/vector_db_qa/base.py
+++ b/langchain/chains/vector_db_qa/base.py
@@ -5,8 +5,9 @@ from pydantic import BaseModel, Extra
 
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
-from langchain.chains.vector_db_qa.prompt import prompt
+from langchain.chains.vector_db_qa.prompt import PROMPT
 from langchain.llms.base import LLM
+from langchain.prompts import PromptTemplate
 from langchain.vectorstores.base import VectorStore
 
 
@@ -29,6 +30,8 @@ class VectorDBQA(Chain, BaseModel):
     """Vector Database to connect to."""
     k: int = 4
     """Number of documents to query for."""
+    prompt: PromptTemplate = PROMPT
+    """Prompt to use when questioning the documents."""
     input_key: str = "query"  #: :meta private:
     output_key: str = "result"  #: :meta private:
 
@@ -56,7 +59,7 @@ class VectorDBQA(Chain, BaseModel):
 
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
         question = inputs[self.input_key]
-        llm_chain = LLMChain(llm=self.llm, prompt=prompt)
+        llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
         docs = self.vectorstore.similarity_search(question, k=self.k)
         contexts = []
         for j, doc in enumerate(docs):
diff --git a/langchain/chains/vector_db_qa/prompt.py b/langchain/chains/vector_db_qa/prompt.py
index 734223a36..9ebb89eac 100644
--- a/langchain/chains/vector_db_qa/prompt.py
+++ b/langchain/chains/vector_db_qa/prompt.py
@@ -7,6 +7,6 @@ prompt_template = """Use the following pieces of context to answer the question
 
 Question: {question}
 Helpful Answer:"""
-prompt = PromptTemplate(
+PROMPT = PromptTemplate(
     template=prompt_template, input_variables=["context", "question"]
 )