diff --git a/dspy/propose/dataset_summary_generator.py b/dspy/propose/dataset_summary_generator.py
index 50bcff27c7..f0fc7611bb 100644
--- a/dspy/propose/dataset_summary_generator.py
+++ b/dspy/propose/dataset_summary_generator.py
@@ -70,7 +70,8 @@ def create_dataset_summary(trainset, view_data_batch_size, prompt_model, log_fil
             upper_lim = min(len(trainset), b+view_data_batch_size)
             with dspy.context(lm=prompt_model):
                 output = dspy.Predict(DatasetDescriptorWithPriorObservations, n=1, temperature=1.0)(prior_observations=observations, examples=order_input_keys_in_string(trainset[b:upper_lim].__repr__()))
-            if len(output["observations"]) >= 8 and output["observations"][:8].upper() == "COMPLETE":
+            obs = output["observations"].strip().strip("\"'").strip()
+            if obs.upper().startswith("COMPLETE") and (len(obs) == 8 or not obs[8].isalpha()):
                 skips += 1
                 if skips >= 5:
                     break
diff --git a/tests/propose/test_dataset_summary_generator.py b/tests/propose/test_dataset_summary_generator.py
new file mode 100644
index 0000000000..6cb530cca9
--- /dev/null
+++ b/tests/propose/test_dataset_summary_generator.py
@@ -0,0 +1,84 @@
+import pytest
+
+import dspy
+from dspy.propose.dataset_summary_generator import create_dataset_summary
+from dspy.utils.dummies import DummyLM
+
+
+def _build_trainset(n):
+    return [dspy.Example(question=f"What is {i}+{i}?", answer=f"{2 * i}") for i in range(n)]
+
+
+def _summarizer_prompt(trainset, answers):
+    """Run ``create_dataset_summary`` and return the prompt sent to ObservationSummarizer (last LM call)."""
+    lm = DummyLM(answers)
+    create_dataset_summary(trainset=trainset, view_data_batch_size=10, prompt_model=lm)
+    messages, _ = lm.get_convo(len(lm.history) - 1)
+    return "\n".join(m.get("content", "") for m in messages)
+
+
+# Sentinel forms the detector must recognize and skip (not append to observations).
+# The DatasetDescriptorWithPriorObservations prompt asks the model to ``say 'COMPLETE'``
+# (single-quoted), so quoted forms occur in practice; the detector must strip the quotes.
+SENTINELS = ["COMPLETE", "'COMPLETE'", '"COMPLETE"', "COMPLETE."]
+
+# Real observations whose first 8 chars match "COMPLETE" but must NOT be treated as the sentinel.
+REAL_OBSERVATIONS = [
+    "Completely analyzed the data",
+    "Completed the review of all samples",
+]
+
+
+@pytest.mark.parametrize("sentinel", SENTINELS)
+def test_sentinel_is_skipped_not_appended(sentinel):
+    """A COMPLETE sentinel (bare, quoted, or with trailing punctuation) must be skipped
+    rather than appended to the observations fed to ObservationSummarizer."""
+    # trainset=20 -> 1 loop iteration (range(10, 20, 10) == [10]).
+    prompt = _summarizer_prompt(
+        _build_trainset(20),
+        [
+            {"observations": "Initial observation."},  # initial DatasetDescriptor
+            {"observations": sentinel},  # loop iteration: sentinel, must be skipped
+            {"summary": "the summary"},  # ObservationSummarizer
+        ],
+    )
+    # Case-sensitive "COMPLETE" avoids matching the chat adapter's "[[ ## completed ## ]]" marker.
+    assert "COMPLETE" not in prompt, f"Sentinel {sentinel!r} was appended instead of skipped:\n{prompt}"
+
+
+@pytest.mark.parametrize("observation", REAL_OBSERVATIONS)
+def test_real_observation_starting_with_complete_is_preserved(observation):
+    """A legitimate observation starting with "Complete" must NOT be mistaken for the
+    sentinel; it must be appended and reach ObservationSummarizer."""
+    prompt = _summarizer_prompt(
+        _build_trainset(20),
+        [
+            {"observations": "Initial observation."},
+            {"observations": observation},  # real observation, must be kept
+            {"summary": "the summary"},
+        ],
+    )
+    assert observation in prompt, f"Real observation {observation!r} was dropped as a sentinel:\n{prompt}"
+
+
+def test_repeated_quoted_sentinel_triggers_early_stop():
+    """Quoted ``'COMPLETE'`` on consecutive batches must increment ``skips`` and
+    short-circuit via ``skips >= 5`` instead of running every batch and leaking the quoted
+    tokens into the observations fed to ObservationSummarizer."""
+    # trainset=90 -> 8 loop iterations; 5 quoted sentinels trigger the ``skips >= 5`` early-stop.
+    lm = DummyLM(
+        [{"observations": "Initial observation."}]
+        + [{"observations": "'COMPLETE'"}] * 5
+        + [{"summary": "final summary"}]
+    )
+    summary = create_dataset_summary(
+        trainset=_build_trainset(90),
+        view_data_batch_size=10,
+        prompt_model=lm,
+    )
+    # 1 (initial) + 5 (loop sentinels) + 1 (summarizer) == 7 total LM calls.
+    assert len(lm.history) == 7, f"Expected early-stop at 7 LM calls, got {len(lm.history)}"
+    messages, _ = lm.get_convo(len(lm.history) - 1)
+    summarizer_prompt = "\n".join(m.get("content", "") for m in messages)
+    assert "COMPLETE" not in summarizer_prompt
+    assert summary == "final summary"