diff --git a/docs/getting_started/getting_started.md b/docs/getting_started/getting_started.md
index 9988b3c484..4d276edd30 100644
--- a/docs/getting_started/getting_started.md
+++ b/docs/getting_started/getting_started.md
@@ -355,13 +355,15 @@ Similar to LLMs, you can make use of templating by using a `MessagePromptTemplat
 For convience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
 
 ```python
+from langchain.chat_models import ChatOpenAI
 from langchain.prompts.chat import (
     ChatPromptTemplate,
     SystemMessagePromptTemplate,
-    AIMessagePromptTemplate,
     HumanMessagePromptTemplate,
 )
 
+chat = ChatOpenAI(temperature=0)
+
 template="You are a helpful assistant that translates {input_language} to {output_language}."
 system_message_prompt = SystemMessagePromptTemplate.from_template(template)
 human_template="{text}"
@@ -380,11 +382,10 @@ The `LLMChain` discussed in the above section can be used with chat models as we
 
 ```python
 from langchain.chat_models import ChatOpenAI
-from langchain import PromptTemplate, LLMChain
+from langchain import LLMChain
 from langchain.prompts.chat import (
     ChatPromptTemplate,
     SystemMessagePromptTemplate,
-    AIMessagePromptTemplate,
     HumanMessagePromptTemplate,
 )