diff --git a/examples/data_prep.py b/examples/data_prep.py
new file mode 100644
index 000000000..75305f659
--- /dev/null
+++ b/examples/data_prep.py
@@ -0,0 +1,45 @@
+import concurrent.futures as f
+
+from datasets import DatasetDict, load_dataset
+from langchain.llms import OpenAI
+
+
+def format_for_belar(row):
+    row["context"] = row["selftext"]
+    row["prompt"] = row["title"]
+    row["ground_truth"] = row["answers"]["text"]
+    return row
+
+
+d = load_dataset("eli5")
+assert isinstance(d, DatasetDict)
+ds = d["test_eli5"].map(format_for_belar, batched=False)
+ds = ds.select_columns(["context", "prompt", "ground_truth"])
+
+ds = ds.shuffle(seed=42).select(range(500))
+print(ds.shape, ds.column_names)
+
+
+llm = OpenAI()  # type: ignore
+prompt = """
+{context}
+with the above context explain like I'm five: {prompt}
+"""
+
+
+def get_answers(row):
+    qs, cs = row["prompt"], row["context"]
+
+    generated_answers = []
+    with f.ThreadPoolExecutor(max_workers=10) as executor:
+        results = executor.map(
+            llm, [prompt.format(context=cs[i], prompt=qs[i]) for i in range(len(qs))]
+        )
+        for result in results:
+            generated_answers.append(result)
+
+    row["generated_answers"] = generated_answers
+    return row
+
+
+ds = ds.map(get_answers, batched=True, batch_size=10)
diff --git a/pyproject.toml b/pyproject.toml
index a8a74c469..e1af83e8d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,20 +11,6 @@ dependencies = [
 ]
 dynamic = ["version", "readme"]
 
-[project.optional-dependencies]
-test = [
-    # "pytest-cov",
-    "pytest",
-    "rich",
-]
-
-dev = [
-    "ruff",
-    "isort",
-    "black[jupyter]",
-    "pyright",
-]
-
 [tool.setuptools.dynamic]
 readme = {file = ["README.md"], content-type = "text/plain"}