diff --git a/instructor/__init__.py b/instructor/__init__.py
index 6a659e5..fdee478 100644
--- a/instructor/__init__.py
+++ b/instructor/__init__.py
@@ -1,4 +1,5 @@
 from .function_calls import OpenAISchema, openai_function, openai_schema
+from .distil import FinetuneFormat, distil, track
 from .dsl import MultiTask, Maybe, llm_validator, CitationMixin
 from .patch import patch
 
@@ -11,4 +12,7 @@ __all__ = [
     "openai_schema",
     "patch",
     "llm_validator",
+    "FinetuneFormat",
+    "distil",
+    "track",
 ]
diff --git a/instructor/distil.py b/instructor/distil.py
new file mode 100644
index 0000000..1160e09
--- /dev/null
+++ b/instructor/distil.py
@@ -0,0 +1,212 @@
+import enum
+import functools
+import inspect
+import json
+
+from typing import Any, Callable, Optional
+from pydantic import BaseModel, validate_call
+
+import inspect
+import logging
+
+from instructor import openai_schema
+
+distil_logger = logging.getLogger("instructor.distil")
+
+
+def logging(level=logging.INFO, handler=None, log_to_file=True, filename_prefix=None):
+    """
+    Configure the instructor module's logging.
+
+    :param level: Log level.
+    :param handler: Optional logging handler. If not provided, defaults to FileHandler or NullHandler based on log_to_file.
+    :param log_to_file: If True and no handler is provided, logs to a file.
+    :param filename: Optional filename for logging if log_to_file is True. Defaults to 'instructor.log'.
+    """
+    distil_logger.setLevel(level)
+
+    # Clear existing handlers
+    for h in distil_logger.handlers[:]:
+        distil_logger.removeHandler(h)
+
+    if handler:
+        distil_logger.addHandler(handler)
+    elif log_to_file:
+        filename = filename_prefix or "instructor.log"
+        file_handler = logging.FileHandler(filename)
+        file_handler.setFormatter(logging.Formatter("%(message)s"))
+        distil_logger.addHandler(file_handler)
+    else:
+        distil_logger.addHandler(logging.NullHandler())
+
+
+class FinetuneFormat(enum.Enum):
+    MESSAGES: str = "messages"
+    RAW: str = "raw"
+
+
+def get_signature_from_fn(fn: Callable) -> str:
+    """
+    Get the function signature as a string.
+
+    :Example:
+
+    >>> def my_function(a: int, b: int) -> int:
+    >>>     return a + b
+    >>>
+    >>> get_signature_from_fn(my_function)
+    "def my_function(a: int, b: int) -> int"
+
+    :param fn: Function to get the signature for.
+    :return: Function signature as a string.
+    """
+    sig = inspect.signature(fn)
+    lines = f"def {fn.__name__}{sig}"
+    docstring = inspect.getdoc(fn)
+    if docstring:
+        formatted_docstring = f'"""\n{docstring}\n"""'
+    else:
+        formatted_docstring = ""
+    return f"{lines}\n{formatted_docstring}"
+
+
+@functools.lru_cache()
+def format_function(func: Callable) -> str:
+    """
+    Format a function as a string with docstring and body.
+    """
+    source_lines = inspect.getsourcelines(func)
+    definition = " ".join(source_lines[0]).strip()
+
+    docstring = inspect.getdoc(func)
+    if docstring:
+        formatted_docstring = f'"""\n{docstring}\n"""'
+    else:
+        formatted_docstring = ""
+
+    body = inspect.getsource(func)
+    body = body.replace(f"def {func.__name__}", "")
+
+    return f"{definition}\n{formatted_docstring}\n{body}"
+
+
+def is_return_type_base_model_or_instance(func: Callable[..., Any]) -> bool:
+    """
+    Check if the return type of a function is a pydantic BaseModel or an instance of it.
+
+    :param func: Function to check.
+    :return: True if the return type is a pydantic BaseModel or an instance of it.
+    """
+    return_type = inspect.signature(func).return_annotation
+    return inspect.isclass(return_type) and issubclass(return_type, BaseModel)
+
+
+@validate_call
+def track(
+    fn: Callable[..., Any],
+    args: tuple,
+    kwargs: dict,
+    resp: BaseModel,
+    name: Optional[str] = None,
+    finetune_format: FinetuneFormat = FinetuneFormat.RAW,
+):
+    """
+    Track the function call and response in a log file, later used for finetuning.
+
+    :param fn: Function to track.
+    :param args: Arguments passed to the function.
+    :param kwargs: Keyword arguments passed to the function.
+    :param resp: Response returned by the function.
+    :param name: Name of the function to track. Defaults to the function name.
+    :param finetune_format: Format to use for finetuning. Defaults to "raw".
+    """
+    name = name if name else fn.__name__
+    base_model: BaseModel = type(resp)
+
+    if finetune_format == FinetuneFormat.RAW:
+        function_body = dict(
+            fn_name=name,
+            fn_repr=format_function(fn),
+            args=args,
+            kwargs=kwargs,
+            resp=resp.model_dump(),
+            schema=base_model.model_json_schema(),
+        )
+        distil_logger.info(json.dumps(function_body))
+        return
+
+    if finetune_format == FinetuneFormat.MESSAGES:
+        # This is the format that OpenAI's API expects for a finetune call
+        openai_function_call = openai_schema(base_model).openai_schema
+        function_definition = get_signature_from_fn(fn)
+        function_body = {
+            "messages": [
+                {
+                    "role": "system",
+                    "content": f"Return the response from the function call.\n\n {function_definition}",
+                },
+                {
+                    "role": "user",
+                    "content": f"Return the results of the function with the following arguments:\n\n {name}(*{args}, **{kwargs})",
+                },
+                {
+                    "role": "function",
+                    "function_call": {
+                        "name": openai_function_call["name"],
+                        "augments": resp.model_dump(),
+                    },
+                },
+            ],
+            "functions": [openai_function_call],
+            "function_call": {"name": name},
+        }
+        distil_logger.info(json.dumps(function_body))
+        return
+    raise ValueError(f"Invalid finetune format: {finetune_format}")
+
+
+def distil(
+    *args,
+    name: str = None,
+    mode: str = "distil",
+    fine_tune_format: FinetuneFormat = FinetuneFormat.RAW,
+):
+    """
+    Decorator to track the function call and response, supports distillation and dispatch modes.
+
+    If used without arguments, it must be used as a decorator.
+
+    :Example:
+
+    >>> @distil
+    >>> def my_function() -> MyModel:
+    >>>     return MyModel()
+    >>>
+    >>> @distil(name="my_function")
+    >>> def my_function() -> MyModel:
+    >>>     return MyModel()
+
+    :param fn: Function to track.
+    :param name: Name of the function to track. Defaults to the function name.
+    :param mode: Mode to use for distillation. Defaults to "distil".
+    """
+    allowed_modes = {"distil", "dispatch"}
+    assert mode in allowed_modes, f"Must be in {allowed_modes}"
+    assert mode == "distil", "Only distil mode is supported at the moment."
+
+    def _wrap_distil(fn):
+        msg = f"Return type hint for {fn} must subclass `pydantic.BaseModel'"
+        assert is_return_type_base_model_or_instance(fn), msg
+
+        @functools.wraps(fn)
+        def _distil(*args, **kwargs):
+            resp = fn(*args, **kwargs)
+            track(fn, args, kwargs, resp, name=name, finetune_format=fine_tune_format)
+            return resp
+
+        return _distil
+
+    if len(args) == 1 and callable(args[0]):
+        return _wrap_distil(args[0])
+
+    return _wrap_distil
diff --git a/tests/test_distil.py b/tests/test_distil.py
new file mode 100644
index 0000000..c07bd62
--- /dev/null
+++ b/tests/test_distil.py
@@ -0,0 +1,69 @@
+from pydantic import BaseModel
+from instructor.distil import (
+    distil,
+    format_function,
+    get_signature_from_fn,
+    is_return_type_base_model_or_instance,
+)
+
+# Replace `your_module_name` with your actual module name
+
+
+class SimpleModel(BaseModel):
+    data: int
+
+
+def test_is_return_type_base_model_or_instance():
+    def valid_function() -> SimpleModel:
+        return SimpleModel(data=1)
+
+    def invalid_function() -> int:
+        return 1
+
+    assert is_return_type_base_model_or_instance(valid_function)
+    assert not is_return_type_base_model_or_instance(invalid_function)
+
+
+def test_get_signature_from_fn():
+    def test_function(a: int, b: str) -> float:
+        """Sample docstring"""
+        pass
+
+    result = get_signature_from_fn(test_function)
+    expected = "def test_function(a: int, b: str) -> float"
+    assert expected in result
+    assert "Sample docstring" in result
+
+
+def test_format_function():
+    def sample_function(x: int) -> SimpleModel:
+        """This is a docstring."""
+        return SimpleModel(data=x)
+
+    formatted = format_function(sample_function)
+    assert "def sample_function(x: int) -> SimpleModel:" in formatted
+    assert '"""This is a docstring."""' in formatted
+    assert "return SimpleModel(data=x)" in formatted
+
+
+def test_distil_decorator_without_arguments():
+    @distil
+    def test_func(x: int) -> SimpleModel:
+        return SimpleModel(data=x)
+
+    result = test_func(42)
+    assert result.data == 42
+
+
+def test_distil_decorator_with_name_argument():
+    @distil(name="custom_name")
+    def another_test_func(x: int) -> SimpleModel:
+        return SimpleModel(data=x)
+
+    result = another_test_func(55)
+    assert result.data == 55
+
+
+# Mock track function for decorator tests
+def mock_track(*args, **kwargs):
+    pass