diff --git a/packages/jupyter-ai-magics/jupyter_ai_magics/providers.py b/packages/jupyter-ai-magics/jupyter_ai_magics/providers.py
index 5762f2560..3f3712ad7 100644
--- a/packages/jupyter-ai-magics/jupyter_ai_magics/providers.py
+++ b/packages/jupyter-ai-magics/jupyter_ai_magics/providers.py
@@ -11,7 +11,13 @@
 from langchain.chat_models.base import BaseChatModel
 from langchain.llms.sagemaker_endpoint import LLMContentHandler
 from langchain.llms.utils import enforce_stop_tokens
-from langchain.prompts import PromptTemplate
+from langchain.prompts import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    MessagesPlaceholder,
+    PromptTemplate,
+    SystemMessagePromptTemplate,
+)
 from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.schema import LLMResult
 from langchain.utils import get_from_dict_or_env
@@ -42,6 +48,23 @@
     from pydantic.main import ModelMetaclass
 
 
+CHAT_SYSTEM_PROMPT = """
+You are Jupyternaut, a conversational assistant living in JupyterLab to help users.
+You are not a language model, but rather an application built on a foundation model from {provider_name} called {local_model_id}.
+You are talkative and you provide lots of specific details from the foundation model's context.
+You may use Markdown to format your response.
+Code blocks must be formatted in Markdown.
+Math should be rendered with inline TeX markup, surrounded by $.
+If you do not know the answer to a question, answer truthfully by responding that you do not know.
+The following is a friendly conversation between you and a human.
+""".strip()
+
+CHAT_DEFAULT_TEMPLATE = """Current conversation:
+{history}
+Human: {input}
+AI:"""
+
+
 class EnvAuthStrategy(BaseModel):
     """Require one auth token via an environment variable."""
 
@@ -265,6 +288,32 @@ def get_prompt_template(self, format) -> PromptTemplate:
         else:
             return self.prompt_templates["text"]  # Default to plain format
 
+    def get_chat_prompt_template(self) -> PromptTemplate:
+        """
+        Produce a prompt template optimised for chat conversation.
+        The template should take two variables: history and input.
+        """
+        name = self.__class__.name
+        if self.is_chat_provider:
+            return ChatPromptTemplate.from_messages(
+                [
+                    SystemMessagePromptTemplate.from_template(
+                        CHAT_SYSTEM_PROMPT
+                    ).format(provider_name=name, local_model_id=self.model_id),
+                    MessagesPlaceholder(variable_name="history"),
+                    HumanMessagePromptTemplate.from_template("{input}"),
+                ]
+            )
+        else:
+            return PromptTemplate(
+                input_variables=["history", "input"],
+                template=CHAT_SYSTEM_PROMPT.format(
+                    provider_name=name, local_model_id=self.model_id
+                )
+                + "\n\n"
+                + CHAT_DEFAULT_TEMPLATE,
+            )
+
     @property
     def is_chat_provider(self):
         return isinstance(self, BaseChatModel)
diff --git a/packages/jupyter-ai/jupyter_ai/chat_handlers/default.py b/packages/jupyter-ai/jupyter_ai/chat_handlers/default.py
index 0db83afdd..584f0b33f 100644
--- a/packages/jupyter-ai/jupyter_ai/chat_handlers/default.py
+++ b/packages/jupyter-ai/jupyter_ai/chat_handlers/default.py
@@ -4,32 +4,9 @@
 from jupyter_ai_magics.providers import BaseProvider
 from langchain.chains import ConversationChain
 from langchain.memory import ConversationBufferWindowMemory
-from langchain.prompts import (
-    ChatPromptTemplate,
-    HumanMessagePromptTemplate,
-    MessagesPlaceholder,
-    PromptTemplate,
-    SystemMessagePromptTemplate,
-)
 
 from .base import BaseChatHandler, SlashCommandRoutingType
 
-SYSTEM_PROMPT = """
-You are Jupyternaut, a conversational assistant living in JupyterLab to help users.
-You are not a language model, but rather an application built on a foundation model from {provider_name} called {local_model_id}.
-You are talkative and you provide lots of specific details from the foundation model's context.
-You may use Markdown to format your response.
-Code blocks must be formatted in Markdown.
-Math should be rendered with inline TeX markup, surrounded by $.
-If you do not know the answer to a question, answer truthfully by responding that you do not know.
-The following is a friendly conversation between you and a human.
-""".strip()
-
-DEFAULT_TEMPLATE = """Current conversation:
-{history}
-Human: {input}
-AI:"""
-
 
 class DefaultChatHandler(BaseChatHandler):
     id = "default"
@@ -49,27 +26,10 @@ def create_llm_chain(
         model_parameters = self.get_model_parameters(provider, provider_params)
         llm = provider(**provider_params, **model_parameters)
 
-        if llm.is_chat_provider:
-            prompt_template = ChatPromptTemplate.from_messages(
-                [
-                    SystemMessagePromptTemplate.from_template(SYSTEM_PROMPT).format(
-                        provider_name=provider.name, local_model_id=llm.model_id
-                    ),
-                    MessagesPlaceholder(variable_name="history"),
-                    HumanMessagePromptTemplate.from_template("{input}"),
-                ]
-            )
-            self.memory = ConversationBufferWindowMemory(return_messages=True, k=2)
-        else:
-            prompt_template = PromptTemplate(
-                input_variables=["history", "input"],
-                template=SYSTEM_PROMPT.format(
-                    provider_name=provider.name, local_model_id=llm.model_id
-                )
-                + "\n\n"
-                + DEFAULT_TEMPLATE,
-            )
-            self.memory = ConversationBufferWindowMemory(k=2)
+        prompt_template = llm.get_chat_prompt_template()
+        self.memory = ConversationBufferWindowMemory(
+            return_messages=llm.is_chat_provider, k=2
+        )
 
         self.llm = llm
         self.llm_chain = ConversationChain(