diff --git a/llama_toolchain/common/prompt_templates/base.py b/llama_toolchain/common/prompt_templates/base.py
new file mode 100644
index 000000000..de229bcb2
--- /dev/null
+++ b/llama_toolchain/common/prompt_templates/base.py
@@ -0,0 +1,26 @@
+from dataclasses import dataclass
+from typing import Any, Dict, List
+
+from jinja2 import Template
+
+
+@dataclass
+class PromptTemplate:
+    template: str
+    data: Dict[str, Any]
+
+    def render(self):
+        template = Template(self.template)
+        return template.render(self.data)
+
+
+class PromptTemplateGeneratorBase:
+    """
+    Base class for prompt template generators.
+    """
+
+    def gen(self, *args, **kwargs) -> PromptTemplate:
+        raise NotImplementedError()
+
+    def data_examples(self) -> List[Any]:
+        raise NotImplementedError()
diff --git a/llama_toolchain/common/prompt_templates/system_prompts.py b/llama_toolchain/common/prompt_templates/system_prompts.py
new file mode 100644
index 000000000..aeabb116e
--- /dev/null
+++ b/llama_toolchain/common/prompt_templates/system_prompts.py
@@ -0,0 +1,206 @@
+import textwrap
+from datetime import datetime
+from typing import Any, Dict, List
+
+from llama_models.llama3.api.datatypes import (
+    BuiltinTool,
+    ToolDefinition,
+    ToolParamDefinition,
+)
+
+from .base import PromptTemplate, PromptTemplateGeneratorBase
+
+
+class SystemDefaultGenerator(PromptTemplateGeneratorBase):
+
+    def gen(self, *args, **kwargs) -> PromptTemplate:
+        template_str = textwrap.dedent(
+            """
+            Cutting Knowledge Date: December 2023
+            Today Date: {{ today }}
+            """
+        )
+        return PromptTemplate(
+            template_str.lstrip("\n"),
+            {"today": datetime.now().strftime("%d %B %Y")},
+        )
+
+    def data_examples(self) -> List[Any]:
+        return [None]
+
+
+class BuiltinToolGenerator(PromptTemplateGeneratorBase):
+
+    def _tool_breakdown(self, tools: List[ToolDefinition]):
+        builtin_tools, custom_tools = [], []
+        for dfn in tools:
+            if isinstance(dfn.tool_name, BuiltinTool):
+                builtin_tools.append(dfn)
+            else:
+                custom_tools.append(dfn)
+
+        return builtin_tools, custom_tools
+
+    def gen(self, tools: List[ToolDefinition]) -> PromptTemplate:
+        builtin_tools, custom_tools = self._tool_breakdown(tools)
+        data = []
+        template_str = textwrap.dedent(
+            """
+            {% if builtin_tools or custom_tools -%}
+            Environment: ipython
+            {% endif -%}
+            {% set builtin_tools = builtin_tools | reject('equalto', 'code_interpreter') | list -%}
+            {% if builtin_tools -%}
+            Tools: {{ builtin_tools | join(", ") | trim -}}
+            {% endif %}
+            """
+        )
+        return PromptTemplate(
+            template_str.lstrip("\n"),
+            {
+                "builtin_tools": [t.tool_name.value for t in builtin_tools],
+                "custom_tools": custom_tools,
+            },
+        )
+
+    def data_examples(self) -> List[List[ToolDefinition]]:
+        return [
+            # builtin tools
+            [
+                ToolDefinition(tool_name=BuiltinTool.code_interpreter),
+                ToolDefinition(tool_name=BuiltinTool.brave_search),
+                ToolDefinition(tool_name=BuiltinTool.wolfram_alpha),
+            ],
+            # only code interpretor
+            [
+                ToolDefinition(tool_name=BuiltinTool.code_interpreter),
+            ],
+        ]
+
+
+class JsonCustomToolGenerator(PromptTemplateGeneratorBase):
+
+    def gen(self, custom_tools: List[ToolDefinition]) -> PromptTemplate:
+        template_str = textwrap.dedent(
+            """
+            Answer the user's question by making use of the following functions if needed.
+            If none of the function can be used, please say so.
+            Here is a list of functions in JSON format:
+            {% for t in custom_tools -%}
+            {# manually setting up JSON because jinja sorts keys in unexpected ways -#}
+            {%- set tname = t.tool_name -%}
+            {%- set tdesc = t.description -%}
+            {%- set tparams = t.parameters -%}
+            {%- set required_params = [] -%}
+            {%- for name, param in tparams.items() if param.required == true -%}
+                {%- set _ = required_params.append(name) -%}
+            {%- endfor -%}
+            {
+                "type": "function",
+                "function": {
+                    "name": "{{tname}}",
+                    "description": "{{tdesc}}",
+                    "parameters": {
+                        "type": "object",
+                        "properties": [
+                            {%- for name, param in tparams.items() %}
+                            {
+                                "{{name}}": {
+                                    "type": "object",
+                                    "description": "{{param.description}}"
+                                }
+                            }{% if not loop.last %},{% endif %}
+                            {%- endfor %}
+                        ],
+                        "required": {{ required_params | tojson }}
+                    }
+                }
+            }
+            {% endfor %}
+            Return function calls in JSON format.
+            """
+        )
+
+        return PromptTemplate(
+            template_str.lstrip("\n"),
+            {"custom_tools": [t.model_dump() for t in custom_tools]},
+        )
+
+    def data_examples(self) -> List[List[ToolDefinition]]:
+        return [
+            [
+                ToolDefinition(
+                    tool_name="trending_songs",
+                    description="Returns the trending songs on a Music site",
+                    parameters={
+                        "n": ToolParamDefinition(
+                            param_type="int",
+                            description="The number of songs to return",
+                            required=True,
+                        ),
+                        "genre": ToolParamDefinition(
+                            param_type="str",
+                            description="The genre of the songs to return",
+                            required=False,
+                        ),
+                    },
+                ),
+            ]
+        ]
+
+
+class FunctionTagCustomToolGenerator(PromptTemplateGeneratorBase):
+
+    def gen(self, custom_tools: List[ToolDefinition]) -> PromptTemplate:
+        template_str = textwrap.dedent(
+            """
+            You have access to the following functions:
+
+            {% for t in custom_tools %}
+            {#- manually setting up JSON because jinja sorts keys in unexpected ways -#}
+            {%- set tname = t.tool_name -%}
+            {%- set tdesc = t.description -%}
+            {%- set tparams = t.parameters | tojson -%}
+            Use the function '{{ tname }}' to '{{ tdesc }}':
+            {"name": "{{tname}}", "description": "{{tdesc}}", "parameters": {{tparams}}}
+
+            {% endfor -%}
+            Think very carefully before calling functions.
+            If a you choose to call a function ONLY reply in the following format with no prefix or suffix:
+
+            <function=example_function_name>{"example_name": "example_value"}</function>
+
+            Reminder:
+            - If looking for real time information use relevant functions before falling back to brave_search
+            - Function calls MUST follow the specified format, start with <function= and end with </function>
+            - Required parameters MUST be specified
+            - Only call one function at a time
+            - Put the entire function call reply on one line
+            """
+        )
+        return PromptTemplate(
+            template_str.lstrip("\n"),
+            {"custom_tools": [t.model_dump() for t in custom_tools]},
+        )
+
+    def data_examples(self) -> List[List[ToolDefinition]]:
+        return [
+            [
+                ToolDefinition(
+                    tool_name="trending_songs",
+                    description="Returns the trending songs on a Music site",
+                    parameters={
+                        "n": ToolParamDefinition(
+                            param_type="int",
+                            description="The number of songs to return",
+                            required=True,
+                        ),
+                        "genre": ToolParamDefinition(
+                            param_type="str",
+                            description="The genre of the songs to return",
+                            required=False,
+                        ),
+                    },
+                ),
+            ]
+        ]
diff --git a/llama_toolchain/inference/meta_reference/inference.py b/llama_toolchain/inference/meta_reference/inference.py
index dc674a25b..87ffc5226 100644
--- a/llama_toolchain/inference/meta_reference/inference.py
+++ b/llama_toolchain/inference/meta_reference/inference.py
@@ -22,7 +22,7 @@ from llama_toolchain.inference.api import (
     ToolCallDelta,
     ToolCallParseStatus,
 )
-from llama_toolchain.inference.prepare_messages import prepare_messages_for_tools
+from llama_toolchain.inference.prepare_messages import prepare_messages
 from .config import MetaReferenceImplConfig
 from .model_parallel import LlamaModelParallelGenerator
 
@@ -67,7 +67,7 @@ class MetaReferenceInferenceImpl(Inference):
     ) -> AsyncIterator[
         Union[ChatCompletionResponseStreamChunk, ChatCompletionResponse]
     ]:
-        request = prepare_messages_for_tools(request)
+        messages = prepare_messages(request)
         model = resolve_model(request.model)
         if model is None:
             raise RuntimeError(
@@ -99,7 +99,7 @@ class MetaReferenceInferenceImpl(Inference):
             ipython = False
 
             for token_result in self.generator.chat_completion(
-                messages=request.messages,
+                messages=messages,
                 temperature=request.sampling_params.temperature,
                 top_p=request.sampling_params.top_p,
                 max_gen_len=request.sampling_params.max_tokens,
diff --git a/llama_toolchain/inference/ollama/ollama.py b/llama_toolchain/inference/ollama/ollama.py
index 8bfd38a71..235cb20cc 100644
--- a/llama_toolchain/inference/ollama/ollama.py
+++ b/llama_toolchain/inference/ollama/ollama.py
@@ -32,7 +32,7 @@ from llama_toolchain.inference.api import (
     ToolCallDelta,
     ToolCallParseStatus,
 )
-from llama_toolchain.inference.prepare_messages import prepare_messages_for_tools
+from llama_toolchain.inference.prepare_messages import prepare_messages
 from .config import OllamaImplConfig
 
 # TODO: Eventually this will move to the llama cli model list command
@@ -111,7 +111,7 @@ class OllamaInference(Inference):
         return options
 
     async def chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
-        request = prepare_messages_for_tools(request)
+        messages = prepare_messages(request)
         # accumulate sampling params and other options to pass to ollama
         options = self.get_ollama_chat_options(request)
         ollama_model = self.resolve_ollama_model(request.model)
@@ -133,7 +133,7 @@ class OllamaInference(Inference):
         if not request.stream:
             r = await self.client.chat(
                 model=ollama_model,
-                messages=self._messages_to_ollama_messages(request.messages),
+                messages=self._messages_to_ollama_messages(messages),
                 stream=False,
                 options=options,
             )
@@ -161,7 +161,7 @@ class OllamaInference(Inference):
             )
             stream = await self.client.chat(
                 model=ollama_model,
-                messages=self._messages_to_ollama_messages(request.messages),
+                messages=self._messages_to_ollama_messages(messages),
                 stream=True,
                 options=options,
             )
diff --git a/llama_toolchain/inference/prepare_messages.py b/llama_toolchain/inference/prepare_messages.py
index e23bbbe8f..83aff57f9 100644
--- a/llama_toolchain/inference/prepare_messages.py
+++ b/llama_toolchain/inference/prepare_messages.py
@@ -1,203 +1,66 @@
-import json
-import os
 import textwrap
 
-from datetime import datetime
 from llama_toolchain.inference.api import *  # noqa: F403
-from llama_toolchain.tools.builtin import (
-    BraveSearchTool,
-    CodeInterpreterTool,
-    PhotogenTool,
-    WolframAlphaTool,
+from llama_toolchain.common.prompt_templates.system_prompts import (
+    BuiltinToolGenerator,
+    FunctionTagCustomToolGenerator,
+    JsonCustomToolGenerator,
+    SystemDefaultGenerator,
 )
 
 
-def tool_breakdown(tools: List[ToolDefinition]) -> str:
-    builtin_tools, custom_tools = [], []
-    for dfn in tools:
-        if isinstance(dfn.tool_name, BuiltinTool):
-            builtin_tools.append(dfn)
-        else:
-            custom_tools.append(dfn)
+def prepare_messages(request: ChatCompletionRequest) -> List[Message]:
 
-    return builtin_tools, custom_tools
-
-
-def prepare_messages_for_tools(request: ChatCompletionRequest) -> ChatCompletionRequest:
-    """This functions takes a ChatCompletionRequest and returns an augmented request.
-    The request's messages are augmented to update the system message
-    corresponding to the tool definitions provided in the request.
-    """
     assert request.tool_choice == ToolChoice.auto, "Only `ToolChoice.auto` supported"
 
     existing_messages = request.messages
-
     existing_system_message = None
     if existing_messages[0].role == Role.system.value:
         existing_system_message = existing_messages.pop(0)
 
-    builtin_tools, custom_tools = tool_breakdown(request.tools)
+    assert (
+        existing_messages[0].role != Role.system.value
+    ), "Should only have 1 system message"
 
     messages = []
-    content = ""
-    if builtin_tools or custom_tools:
-        content += "Environment: ipython\n"
 
-    if builtin_tools:
-        tool_str = ", ".join(
-            [
-                t.tool_name.value
-                for t in builtin_tools
-                if t.tool_name != BuiltinTool.code_interpreter
-            ]
-        )
-        if tool_str:
-            content += f"Tools: {tool_str}\n"
+    default_gen = SystemDefaultGenerator()
+    default_template = default_gen.gen()
 
-    current_date = datetime.now()
-    formatted_date = current_date.strftime("%d %B %Y")
-    date_str = textwrap.dedent(
-        f"""
-        Cutting Knowledge Date: December 2023
-        Today Date: {formatted_date}
-        """
-    )
-    content += date_str.lstrip("\n")
+    sys_content = ""
+
+    tool_template = None
+    if request.tools:
+        tool_gen = BuiltinToolGenerator()
+        tool_template = tool_gen.gen(request.tools)
+
+        sys_content += tool_template.render()
+        sys_content += "\n"
+
+    sys_content += default_template.render()
 
     if existing_system_message:
-        content += "\n"
-        content += existing_system_message.content
+        sys_content += "\n"
+        sys_content += existing_system_message.content
 
-    messages.append(SystemMessage(content=content))
+    messages.append(SystemMessage(content=sys_content))
 
-    if custom_tools:
-        if request.tool_prompt_format == ToolPromptFormat.function_tag:
-            text = prompt_for_function_tag(custom_tools)
-            messages.append(UserMessage(content=text))
-        elif request.tool_prompt_format == ToolPromptFormat.json:
-            text = prompt_for_json(custom_tools)
-            messages.append(UserMessage(content=text))
+    has_custom_tools = any(isinstance(dfn.tool_name, str) for dfn in request.tools)
+    if has_custom_tools:
+        if request.tool_prompt_format == ToolPromptFormat.json:
+            tool_gen = JsonCustomToolGenerator()
+        elif request.tool_prompt_format == ToolPromptFormat.function_tag:
+            tool_gen = FunctionTagCustomToolGenerator()
         else:
-            raise NotImplementedError(
-                f"Tool prompt format {tool_prompt_format} is not supported"
+            raise ValueError(
+                f"Non supported ToolPromptFormat {request.tool_prompt_format}"
             )
 
+        custom_tools = [t for t in request.tools if isinstance(t.tool_name, str)]
+        custom_template = tool_gen.gen(custom_tools)
+        messages.append(UserMessage(content=custom_template.render()))
+
+    # Add back existing messages from the request
     messages += existing_messages
-    request.messages = messages
-    return request
 
-
-def prompt_for_json(custom_tools: List[ToolDefinition]) -> str:
-    tool_defs = "\n".join(
-        translate_custom_tool_definition_to_json(t) for t in custom_tools
-    )
-    content = textwrap.dedent(
-        """
-        Answer the user's question by making use of the following functions if needed.
-        If none of the function can be used, please say so.
-        Here is a list of functions in JSON format:
-        {tool_defs}
-
-        Return function calls in JSON format.
-        """
-    )
-    content = content.lstrip("\n").format(tool_defs=tool_defs)
-    return content
-
-
-def prompt_for_function_tag(custom_tools: List[ToolDefinition]) -> str:
-    custom_tool_params = ""
-    for t in custom_tools:
-        custom_tool_params += get_instruction_string(t) + "\n"
-        custom_tool_params += get_parameters_string(t) + "\n\n"
-
-    content = textwrap.dedent(
-        """
-        You have access to the following functions:
-
-        {custom_tool_params}
-        Think very carefully before calling functions.
-        If you choose to call a function ONLY reply in the following format with no prefix or suffix:
-
-        <function=example_function_name>{{"example_name": "example_value"}}</function>
-
-        Reminder:
-        - If looking for real time information use relevant functions before falling back to brave_search
-        - Function calls MUST follow the specified format, start with <function= and end with </function>
-        - Required parameters MUST be specified
-        - Only call one function at a time
-        - Put the entire function call reply on one line
-        """
-    )
-
-    return content.lstrip("\n").format(custom_tool_params=custom_tool_params)
-
-
-def get_instruction_string(custom_tool_definition) -> str:
-    return f"Use the function '{custom_tool_definition.tool_name}' to '{custom_tool_definition.description}'"
-
-
-def get_parameters_string(custom_tool_definition) -> str:
-    return json.dumps(
-        {
-            "name": custom_tool_definition.tool_name,
-            "description": custom_tool_definition.description,
-            "parameters": {
-                name: definition.__dict__
-                for name, definition in custom_tool_definition.parameters.items()
-            },
-        }
-    )
-
-
-def translate_custom_tool_definition_to_json(tool_def):
-    """Translates ToolDefinition to json as expected by model
-    eg. output for a function
-    {
-        "type": "function",
-        "function": {
-            "name": "conv_int",
-            "description": "Convert serialized fract24 integer into int value.",
-            "parameters": {
-                "type": "object",
-                "properties": [
-                    {
-                        "data": {
-                            "type": "object",
-                            "description": ""
-                        }
-                    }
-                ],
-                "required": ["data"]
-            }
-        }
-    }
-    """
-    assert isinstance(tool_def.tool_name, str)
-    func_def = {"type": "function", "function": {}}
-    func_def["function"]["name"] = tool_def.tool_name
-    func_def["function"]["description"] = tool_def.description or ""
-    if tool_def.parameters:
-        required = []
-        properties = []
-        for p_name, p_def in tool_def.parameters.items():
-            properties.append(
-                {
-                    p_name: {
-                        # TODO: see if this should not always be object
-                        "type": "object",
-                        "description": p_def.description or "",
-                    }
-                }
-            )
-            if p_def.required:
-                required.append(p_name)
-        func_def["function"]["parameters"] = {
-            "type": "object",
-            "properties": properties,
-            "required": required,
-        }
-    else:
-        func_def["function"]["parameters"] = {}
-
-    return json.dumps(func_def, indent=4)
+    return messages
diff --git a/tests/test_ollama_inference.py b/tests/test_ollama_inference.py
index 72101e25b..5ff1b94f9 100644
--- a/tests/test_ollama_inference.py
+++ b/tests/test_ollama_inference.py
@@ -13,7 +13,10 @@ from llama_models.llama3.api.datatypes import (
     ToolResponseMessage,
     UserMessage,
 )
-from llama_toolchain.inference.api.datatypes import ChatCompletionResponseEventType
+from llama_toolchain.inference.api.datatypes import (
+    ChatCompletionResponseEventType,
+    ToolPromptFormat,
+)
 from llama_toolchain.inference.api.endpoints import ChatCompletionRequest
 from llama_toolchain.inference.ollama.config import OllamaImplConfig
 from llama_toolchain.inference.ollama.ollama import get_provider_impl
@@ -236,6 +239,7 @@ class OllamaInferenceTests(unittest.IsolatedAsyncioTestCase):
             ],
             stream=True,
             tools=[self.custom_tool_defn],
+            tool_prompt_format=ToolPromptFormat.function_tag,
         )
         iterator = self.api.chat_completion(request)
         events = []
diff --git a/tests/test_tool_utils.py b/tests/test_prepare_messages.py
similarity index 61%
rename from tests/test_tool_utils.py
rename to tests/test_prepare_messages.py
index 360c769b1..49624b04d 100644
--- a/tests/test_tool_utils.py
+++ b/tests/test_prepare_messages.py
@@ -2,12 +2,12 @@ import unittest
 
 from llama_models.llama3.api import *  # noqa: F403
 from llama_toolchain.inference.api import *  # noqa: F403
-from llama_toolchain.inference.prepare_messages import prepare_messages_for_tools
+from llama_toolchain.inference.prepare_messages import prepare_messages
 
 MODEL = "Meta-Llama3.1-8B-Instruct"
 
 
-class ToolUtilsTests(unittest.IsolatedAsyncioTestCase):
+class PrepareMessagesTests(unittest.IsolatedAsyncioTestCase):
     async def test_system_default(self):
         content = "Hello !"
         request = ChatCompletionRequest(
@@ -16,12 +16,10 @@ class ToolUtilsTests(unittest.IsolatedAsyncioTestCase):
                 UserMessage(content=content),
             ],
         )
-        request = prepare_messages_for_tools(request)
-        self.assertEqual(len(request.messages), 2)
-        self.assertEqual(request.messages[-1].content, content)
-        self.assertTrue(
-            "Cutting Knowledge Date: December 2023" in request.messages[0].content
-        )
+        messages = prepare_messages(request)
+        self.assertEqual(len(messages), 2)
+        self.assertEqual(messages[-1].content, content)
+        self.assertTrue("Cutting Knowledge Date: December 2023" in messages[0].content)
 
     async def test_system_builtin_only(self):
         content = "Hello !"
@@ -35,13 +33,11 @@ class ToolUtilsTests(unittest.IsolatedAsyncioTestCase):
                 ToolDefinition(tool_name=BuiltinTool.brave_search),
             ],
         )
-        request = prepare_messages_for_tools(request)
-        self.assertEqual(len(request.messages), 2)
-        self.assertEqual(request.messages[-1].content, content)
-        self.assertTrue(
-            "Cutting Knowledge Date: December 2023" in request.messages[0].content
-        )
-        self.assertTrue("Tools: brave_search" in request.messages[0].content)
+        messages = prepare_messages(request)
+        self.assertEqual(len(messages), 2)
+        self.assertEqual(messages[-1].content, content)
+        self.assertTrue("Cutting Knowledge Date: December 2023" in messages[0].content)
+        self.assertTrue("Tools: brave_search" in messages[0].content)
 
     async def test_system_custom_only(self):
         content = "Hello !"
@@ -65,14 +61,12 @@ class ToolUtilsTests(unittest.IsolatedAsyncioTestCase):
             ],
             tool_prompt_format=ToolPromptFormat.json,
         )
-        request = prepare_messages_for_tools(request)
-        self.assertEqual(len(request.messages), 3)
-        self.assertTrue("Environment: ipython" in request.messages[0].content)
+        messages = prepare_messages(request)
+        self.assertEqual(len(messages), 3)
+        self.assertTrue("Environment: ipython" in messages[0].content)
 
-        self.assertTrue(
-            "Return function calls in JSON format" in request.messages[1].content
-        )
-        self.assertEqual(request.messages[-1].content, content)
+        self.assertTrue("Return function calls in JSON format" in messages[1].content)
+        self.assertEqual(messages[-1].content, content)
 
     async def test_system_custom_and_builtin(self):
         content = "Hello !"
@@ -97,16 +91,14 @@ class ToolUtilsTests(unittest.IsolatedAsyncioTestCase):
                 ),
             ],
         )
-        request = prepare_messages_for_tools(request)
-        self.assertEqual(len(request.messages), 3)
+        messages = prepare_messages(request)
+        self.assertEqual(len(messages), 3)
 
-        self.assertTrue("Environment: ipython" in request.messages[0].content)
-        self.assertTrue("Tools: brave_search" in request.messages[0].content)
+        self.assertTrue("Environment: ipython" in messages[0].content)
+        self.assertTrue("Tools: brave_search" in messages[0].content)
 
-        self.assertTrue(
-            "Return function calls in JSON format" in request.messages[1].content
-        )
-        self.assertEqual(request.messages[-1].content, content)
+        self.assertTrue("Return function calls in JSON format" in messages[1].content)
+        self.assertEqual(messages[-1].content, content)
 
     async def test_user_provided_system_message(self):
         content = "Hello !"
@@ -121,8 +113,8 @@ class ToolUtilsTests(unittest.IsolatedAsyncioTestCase):
                 ToolDefinition(tool_name=BuiltinTool.code_interpreter),
             ],
         )
-        request = prepare_messages_for_tools(request)
-        self.assertEqual(len(request.messages), 2, request.messages)
-        self.assertTrue(request.messages[0].content.endswith(system_prompt))
+        messages = prepare_messages(request)
+        self.assertEqual(len(messages), 2, messages)
+        self.assertTrue(messages[0].content.endswith(system_prompt))
 
-        self.assertEqual(request.messages[-1].content, content)
+        self.assertEqual(messages[-1].content, content)
diff --git a/tests/test_prompt_templates.py b/tests/test_prompt_templates.py
new file mode 100644
index 000000000..94825e327
--- /dev/null
+++ b/tests/test_prompt_templates.py
@@ -0,0 +1,101 @@
+import textwrap
+import unittest
+from datetime import datetime
+
+from llama_toolchain.common.prompt_templates.system_prompts import (
+    BuiltinToolGenerator,
+    FunctionTagCustomToolGenerator,
+    JsonCustomToolGenerator,
+    SystemDefaultGenerator,
+)
+
+
+class PromptTemplateTests(unittest.TestCase):
+
+    def check_generator_output(self, generator, expected_text):
+        example = generator.data_examples()[0]
+
+        pt = generator.gen(example)
+        text = pt.render()
+        # print(text)  # debugging
+        self.assertEqual(text, expected_text)
+
+    def test_system_default(self):
+        generator = SystemDefaultGenerator()
+        today = datetime.now().strftime("%d %B %Y")
+        expected_text = f"Cutting Knowledge Date: December 2023\nToday Date: {today}"
+        self.check_generator_output(generator, expected_text)
+
+    def test_system_builtin_only(self):
+        generator = BuiltinToolGenerator()
+        expected_text = textwrap.dedent(
+            """
+            Environment: ipython
+            Tools: brave_search, wolfram_alpha
+            """
+        )
+        self.check_generator_output(generator, expected_text.strip("\n"))
+
+    def test_system_custom_only(self):
+        self.maxDiff = None
+        generator = JsonCustomToolGenerator()
+        expected_text = textwrap.dedent(
+            """
+            Answer the user's question by making use of the following functions if needed.
+            If none of the function can be used, please say so.
+            Here is a list of functions in JSON format:
+            {
+                "type": "function",
+                "function": {
+                    "name": "trending_songs",
+                    "description": "Returns the trending songs on a Music site",
+                    "parameters": {
+                        "type": "object",
+                        "properties": [
+                            {
+                                "n": {
+                                    "type": "object",
+                                    "description": "The number of songs to return"
+                                }
+                            },
+                            {
+                                "genre": {
+                                    "type": "object",
+                                    "description": "The genre of the songs to return"
+                                }
+                            }
+                        ],
+                        "required": ["n"]
+                    }
+                }
+            }
+
+            Return function calls in JSON format.
+            """
+        )
+        self.check_generator_output(generator, expected_text.strip("\n"))
+
+    def test_system_custom_function_tag(self):
+        self.maxDiff = None
+        generator = FunctionTagCustomToolGenerator()
+        expected_text = textwrap.dedent(
+            """
+            You have access to the following functions:
+
+            Use the function 'trending_songs' to 'Returns the trending songs on a Music site':
+            {"name": "trending_songs", "description": "Returns the trending songs on a Music site", "parameters": {"genre": {"description": "The genre of the songs to return", "param_type": "str", "required": false}, "n": {"description": "The number of songs to return", "param_type": "int", "required": true}}}
+
+            Think very carefully before calling functions.
+            If a you choose to call a function ONLY reply in the following format with no prefix or suffix:
+
+            <function=example_function_name>{"example_name": "example_value"}</function>
+
+            Reminder:
+            - If looking for real time information use relevant functions before falling back to brave_search
+            - Function calls MUST follow the specified format, start with <function= and end with </function>
+            - Required parameters MUST be specified
+            - Only call one function at a time
+            - Put the entire function call reply on one line
+            """
+        )
+        self.check_generator_output(generator, expected_text.strip("\n"))