diff --git a/projects/pgai/tests/vectorizer/cassettes/test_recursive_character_splitting.yaml b/projects/pgai/tests/vectorizer/cassettes/test_recursive_character_splitting.yaml
new file mode 100644
index 000000000..e5746cf77
--- /dev/null
+++ b/projects/pgai/tests/vectorizer/cassettes/test_recursive_character_splitting.yaml
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+      - text-embedding-ada-002
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diff --git a/projects/pgai/tests/vectorizer/conftest.py b/projects/pgai/tests/vectorizer/conftest.py
index 84e54816e..5532b802a 100644
--- a/projects/pgai/tests/vectorizer/conftest.py
+++ b/projects/pgai/tests/vectorizer/conftest.py
@@ -1,5 +1,6 @@
 import os
 from pathlib import Path
+from typing import Any
 
 import pytest
 import tiktoken
@@ -29,6 +30,15 @@ def __env_setup():  # type:ignore
     os.environ.update(original_env)
 
 
+def remove_set_cookie_header(response: dict[str, Any]):
+    headers = response["headers"]
+    headers_to_remove = ["set-cookie", "Set-Cookie"]
+    for header in headers_to_remove:
+        if header in headers:
+            del headers[header]
+    return response
+
+
 @pytest.fixture(scope="session")
 def vcr_():
     cassette_library_dir = Path(__file__).parent.joinpath("cassettes")
@@ -39,6 +49,7 @@ def vcr_():
         record_mode=vcr.mode.ONCE,
         filter_headers=["authorization"],
         match_on=["method", "scheme", "host", "port", "path", "query", "body"],
+        before_record_response=remove_set_cookie_header,
     )
 
 
diff --git a/projects/pgai/tests/vectorizer/test_vectorizer_cli.py b/projects/pgai/tests/vectorizer/test_vectorizer_cli.py
index 7edb24944..25ae2f361 100644
--- a/projects/pgai/tests/vectorizer/test_vectorizer_cli.py
+++ b/projects/pgai/tests/vectorizer/test_vectorizer_cli.py
@@ -499,3 +499,107 @@ def test_vectorizer_picks_up_new_vectorizer(
         else:
             break
     process.terminate()
+
+
+@pytest.mark.parametrize(
+    "test_params",
+    [
+        (
+            2,
+            1,
+            2,
+            "chunking_recursive_character_text_splitter('content', 100, 20,"
+            " separators => array[E'\\n\\n', E'\\n', ' '])",
+            "formatting_python_template('$chunk')",
+        )
+    ],
+)
+def test_recursive_character_splitting(
+    cli_db: tuple[PostgresContainer, Connection],
+    cli_db_url: str,
+    configured_vectorizer_and_source_table: int,
+    monkeypatch: pytest.MonkeyPatch,
+    vcr_: Any,
+):
+    """Test that recursive character splitting correctly chunks
+    content based on natural boundaries"""
+    _, conn = cli_db
+
+    # Given content with natural splitting points
+    sample_content = """Introduction to Machine Learning
+
+Machine learning is a subset of artificial intelligence that focuses on data and
+algorithms.
+It enables systems to learn and improve from experience.
+
+Key Concepts:
+1. Supervised Learning
+2. Unsupervised Learning
+3. Reinforcement Learning
+
+Each type has its own unique applications and methodologies."""
+
+    shorter_content = "This is a shorter post that shouldn't need splitting."
+
+    # Update the test data with our structured content
+    with conn.cursor(row_factory=dict_row) as cur:
+        cur.execute("UPDATE blog SET content = %s WHERE id = 1", (sample_content,))
+        cur.execute("UPDATE blog SET content = %s WHERE id = 2", (shorter_content,))
+
+    monkeypatch.setenv("OPENAI_API_KEY", "test-key")
+
+    # When running the worker
+    with vcr_.use_cassette("test_recursive_character_splitting.yaml"):
+        result = CliRunner().invoke(
+            vectorizer_worker,
+            [
+                "--db-url",
+                cli_db_url,
+                "--once",
+                "--vectorizer-id",
+                str(configured_vectorizer_and_source_table),
+            ],
+            catch_exceptions=False,
+        )
+
+    assert result.exit_code == 0
+
+    # Then verify the chunks were created correctly
+    with conn.cursor(row_factory=dict_row) as cur:
+        cur.execute("""
+            SELECT id, chunk_seq, chunk
+            FROM blog_embedding_store
+            ORDER BY id, chunk_seq
+        """)
+        chunks = cur.fetchall()
+
+        # Verify we got multiple chunks for the longer content
+        first_doc_chunks = [c for c in chunks if c["id"] == 1]
+        assert (
+            len(first_doc_chunks) > 1
+        ), "Long document should be split into multiple chunks"
+
+        # Verify chunk boundaries align with natural breaks
+        assert any(
+            "Introduction to Machine Learning" in c["chunk"] for c in first_doc_chunks
+        ), "Should have a chunk starting with the title"
+
+        assert not all(
+            "Introduction to Machine Learning" in c["chunk"] for c in first_doc_chunks
+        ), "Not all chunks should start with the title"
+
+        assert any(
+            "Key Concepts:" in c["chunk"] for c in first_doc_chunks
+        ), "Should have a chunk containing the key concepts section"
+
+        # Verify shorter content remains as single chunk
+        second_doc_chunks = [c for c in chunks if c["id"] == 2]
+        assert len(second_doc_chunks) == 1, "Short document should be a single chunk"
+        assert second_doc_chunks[0]["chunk"] == shorter_content
+
+        # Verify chunk sequences are correct
+        for doc_chunks in [first_doc_chunks, second_doc_chunks]:
+            sequences = [c["chunk_seq"] for c in doc_chunks]
+            assert sequences == list(
+                range(len(sequences))
+            ), "Chunk sequences should be sequential starting from 0"