diff --git a/tests/functional/__init__.py b/tests/functional/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/functional/test_granular_api.py b/tests/functional/test_granular_api.py
new file mode 100644
index 00000000..79207368
--- /dev/null
+++ b/tests/functional/test_granular_api.py
@@ -0,0 +1,101 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# Standard
+from datetime import datetime
+from unittest.mock import MagicMock
+import glob
+import pathlib
+
+# Third Party
+import git
+
+# First Party
+from instructlab.sdg import BlockRegistry
+from instructlab.sdg.generate_data import (
+    generate_taxonomy,
+    mix_datasets,
+    postprocess_taxonomy,
+    preprocess_taxonomy,
+)
+
+# Local
+from ..mockllmblock import MockLLMBlock
+
+
+def _clone_instructlab_taxonomy(taxonomy_dir):
+    taxonomy_repo_url = "https://github.com/instructlab/taxonomy"
+    taxonomy_commit = "dfa3afaf26f40f923cf758389719619ec9b1ddb1"
+    repo = git.Repo.clone_from(taxonomy_repo_url, taxonomy_dir, no_checkout=True)
+    repo.git.checkout(taxonomy_commit)
+
+
+def test_granular_api_end_to_end(testdata_path: pathlib.Path, tmp_path: pathlib.Path):
+    # Registry our mock block so we can reference it in pipelines
+    BlockRegistry.register("MockLLMBlock")(MockLLMBlock)
+
+    # Clone a taxonomy and edit 1 file in it
+    taxonomy_dir = tmp_path.joinpath("taxonomy")
+    _clone_instructlab_taxonomy(taxonomy_dir)
+    changed_qna_yaml = taxonomy_dir.joinpath(
+        "knowledge", "science", "animals", "birds", "black_capped_chickadee", "qna.yaml"
+    )
+    with open(changed_qna_yaml, "a", encoding="utf-8") as file:
+        file.write("")
+
+    pipeline_dir = testdata_path.joinpath("mock_pipelines")
+    date_suffix = datetime.now().replace(microsecond=0).isoformat().replace(":", "_")
+
+    preprocessed_dir = tmp_path.joinpath("preprocessed")
+    preprocess_taxonomy(
+        taxonomy_dir=taxonomy_dir,
+        output_dir=preprocessed_dir,
+    )
+    chickadee_docs = glob.glob(
+        str(
+            preprocessed_dir.joinpath(
+                "documents", "knowledge_science_*", "chickadee.md"
+            )
+        )
+    )
+    assert chickadee_docs
+    chickadee_samples_path = preprocessed_dir.joinpath(
+        "knowledge_science_animals_birds_black_capped_chickadee.jsonl"
+    )
+    assert chickadee_samples_path.is_file()
+
+    client = MagicMock()
+    client.server_supports_batched = False
+    generated_dir = tmp_path.joinpath("generated")
+    generate_taxonomy(
+        client=client,
+        input_dir=preprocessed_dir,
+        output_dir=generated_dir,
+        pipeline=pipeline_dir,
+    )
+    generated_chickadee_samples_path = generated_dir.joinpath(
+        "knowledge_science_animals_birds_black_capped_chickadee.jsonl"
+    )
+    assert generated_chickadee_samples_path.is_file()
+
+    postprocessed_dir = tmp_path.joinpath("postprocessed")
+    postprocess_taxonomy(
+        input_dir=generated_dir,
+        output_dir=postprocessed_dir,
+        date_suffix=date_suffix,
+        pipeline=pipeline_dir,
+    )
+    knowledge_recipe_file = postprocessed_dir.joinpath(
+        f"knowledge_recipe_{date_suffix}.yaml"
+    )
+    assert knowledge_recipe_file.is_file()
+    skills_recipe_file = postprocessed_dir.joinpath(f"skills_recipe_{date_suffix}.yaml")
+    assert skills_recipe_file.is_file()
+
+    mixed_skills_output_file = (
+        f"{postprocessed_dir}/skills_train_msgs_{date_suffix}.jsonl"
+    )
+    mix_datasets(
+        recipe_file=f"{postprocessed_dir}/skills_recipe_{date_suffix}.yaml",
+        output_file=mixed_skills_output_file,
+    )
+    assert pathlib.Path(mixed_skills_output_file).is_file()
diff --git a/tests/mockllmblock.py b/tests/mockllmblock.py
new file mode 100644
index 00000000..744cd6d5
--- /dev/null
+++ b/tests/mockllmblock.py
@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# Standard
+import random
+import string
+
+# Third Party
+from datasets import Dataset
+
+# First Party
+from instructlab.sdg import LLMBlock
+
+
+def _random_string(size):
+    return "".join(random.choices(string.ascii_lowercase, k=size))
+
+
+def _add_mocked_cols(sample, block_name):
+    match block_name:
+        case "gen_questions" | "gen_grounded_questions":
+            sample["question"] = f"Is this a question {_random_string(8)}?"
+        case "eval_questions" | "eval_grounded_questions":
+            sample["evaluation"] = "This is an evaluation."
+            sample["score"] = "1"
+        case "gen_responses" | "gen_grounded_responses":
+            sample["response"] = "This is a response."
+        case "evaluate_qa_pair" | "evaluate_grounded_qa_pair":
+            sample["evaluation"] = "This is an evaluation."
+            sample["score"] = "2"
+        case "gen_contexts":
+            sample["context"] = f"This is a context {_random_string(8)}."
+        case "gen_spellcheck":
+            sample["spellcheck"] = sample["document"]
+        case "gen_knowledge":
+            sample["question"] = f"Is this a question {_random_string(8)}?"
+            sample["response"] = "This is a response."
+        case "eval_faithfulness_qa_pair":
+            sample["explanation"] = "This is an explanation."
+            sample["judgment"] = "YES"
+        case "eval_relevancy_qa_pair":
+            sample["feedback"] = "This is some feedback."
+            sample["score"] = "2"
+        case "eval_verify_question":
+            sample["explanation"] = "This is an explanation."
+            sample["rating"] = "1"
+        case _:
+            raise Exception(
+                f"Received an un-mocked LLMBlock: {block_name}. Add code in {__file__} to handle this block."
+            )
+    return sample
+
+
+class MockLLMBlock(LLMBlock):
+    def generate(self, samples: Dataset):
+        return samples.map(_add_mocked_cols, fn_kwargs={"block_name": self.block_name})