diff --git a/tests/predict/test_predict.py b/tests/predict/test_predict.py
index c701b380a..bd07e776e 100644
--- a/tests/predict/test_predict.py
+++ b/tests/predict/test_predict.py
@@ -1,11 +1,14 @@
-import dspy
-from dspy import Predict, Signature, TypedPredictor
-from dspy.utils.dummies import DummyLM
 import copy
 import textwrap
+
 import pydantic
+import pytest
 import ujson
 
+import dspy
+from dspy import Predict, Signature, TypedPredictor
+from dspy.utils.dummies import DummyLM
+
 
 def test_initialization_with_string_signature():
     signature_string = "input1, input2 -> output"
@@ -209,14 +212,203 @@ class OutputOnlySignature(dspy.Signature):
     assert lm.get_convo(-1) == textwrap.dedent(
         """\
         Given the fields , produce the fields `output`.
-        
+
         ---
-        
+
         Follow the following format.
-        
+
         Output: ${output}
-        
+
         ---
-        
+
         Output: short answer"""
     )
+
+
+@pytest.fixture(name="SandwichIdea")
+def sandwich_idea_signature():
+    class SandwichIdea(dspy.Signature):
+        """Based on the meal and dietary requirements, suggest a sandwich idea."""
+
+        meal: str = dspy.InputField()
+        dietary_requiements: str = dspy.InputField()
+        bread: str = dspy.OutputField()
+        protein: str = dspy.OutputField()
+        fat: str = dspy.OutputField()
+        garnish: str = dspy.OutputField()
+        sauce: str = dspy.OutputField()
+
+    return SandwichIdea
+
+
+def test_extend_generation(SandwichIdea):
+    lm = DummyLM(
+        [
+            " whole wheat\n\nProtein: turkey\n\nFat: avocado",
+            # Incomplete generation leads to tomato field being assigned as an
+            # empty string ("") in dsp.primitives.predict l98 the generation
+            # therefores continues with the next field.
+            " tomato \n\nSauce: mustard",
+        ]
+    )
+    dspy.settings.configure(lm=lm)
+
+    prediction = Predict(SandwichIdea)(meal="lunch", dietary_requiements="N/A")
+    # The logged conversation (additional newlines removed, [..] indicates the generation):
+    # === DummyLM ===
+    # Based on the meal and dietary requirements, suggest a sandwich idea.
+    # ---
+    # Follow the following format.
+    # Meal: ${meal}
+    # Dietary Requiements: ${dietary_requiements}
+    # Bread: ${bread}
+    # Protein: ${protein}
+    # Fat: ${fat}
+    # Garnish: ${garnish}
+    # Sauce: ${sauce}
+    # ---
+    # Meal: lunch
+    # Dietary Requiements: N/A
+    # Bread: [whole wheat
+    # Protein: turkey
+    # Fat: avocado]
+    # ===
+    # === DummyLM ===
+    # Based on the meal and dietary requirements, suggest a sandwich idea.
+    # ---
+    # Follow the following format.
+    # Meal: ${meal}
+    # Dietary Requiements: ${dietary_requiements}
+    # Bread: ${bread}
+    # Protein: ${protein}
+    # Fat: ${fat}
+    # Garnish: ${garnish}
+    # Sauce: ${sauce}
+    # ---
+    # Meal: lunch
+    # Dietary Requiements: N/A
+    # Bread: whole wheat
+    # Protein: turkey
+    # Fat: avocado
+    # Garnish: [tomato
+    # Sauce: mustard]
+    # ===
+
+    assert prediction.bread == "whole wheat"
+    assert prediction.protein == "turkey"
+    assert prediction.fat == "avocado"
+    assert prediction.garnish == ""  # This field is assigned as "" when the generation is extended
+    assert prediction.sauce == "tomato \n\nSauce: mustard"
+
+
+def test_extend_generation_rolled_back_when_field_is_skipped(SandwichIdea):
+    lm = DummyLM(
+        [
+            " white\n\nFat: butter\n\nGarnish: lettuce\n\nSauce: mayo",
+            " ham\n\nFat: butter\n\nGarnish: lettuce\n\nSauce: mayo",
+        ]
+    )
+    dspy.settings.configure(lm=lm)
+    # The logged conversation (additional newlines removed, [..] indicates the generation):
+    # === DummyLM ===
+    # Based on the meal and dietary requirements, suggest a sandwich idea.
+    # ---
+    # Follow the following format.
+    # Meal: ${meal}
+    # Dietary Requiements: ${dietary_requiements}
+    # Bread: ${bread}
+    # Protein: ${protein}
+    # Fat: ${fat}
+    # Garnish: ${garnish}
+    # Sauce: ${sauce}
+    # ---
+    # Meal: lunch
+    # Dietary Requiements: N/A
+    # Bread:[ white
+    # Fat: butter
+    # Garnish: lettuce
+    # Sauce: mayo]
+    # ===
+    # === DummyLM ===
+    # Based on the meal and dietary requirements, suggest a sandwich idea.
+    # ---
+    # Follow the following format.
+    # Meal: ${meal}
+    # Dietary Requiements: ${dietary_requiements}
+    # Bread: ${bread}
+    # Protein: ${protein}
+    # Fat: ${fat}
+    # Garnish: ${garnish}
+    # Sauce: ${sauce}
+    # ---
+    # Meal: lunch
+    # Dietary Requiements: N/A
+    # Bread: white Fat: butter Garnish: lettuce Sauce: mayo
+    # Protein:[ ham
+    # Fat: butter
+    # Garnish: lettuce
+    # Sauce: mayo]
+    # ===
+
+    predictor = Predict(SandwichIdea)(meal="lunch", dietary_requiements="N/A")
+    assert predictor.bread == "white\n\nFat: butter\n\nGarnish: lettuce\n\nSauce: mayo"
+    assert predictor.protein == ""
+    assert predictor.fat == "ham\n\nFat: butter"
+    assert predictor.garnish == "lettuce"
+    assert predictor.sauce == "mayo"
+
+
+def test_extend_generation_with_empty_field(SandwichIdea):
+    lm = DummyLM(
+        [
+            " white\n\nProtein: \n\nFat: butter\n\nGarnish: lettuce",
+            " lettuce \n\nSauce: mayo",
+        ]
+    )
+    dspy.settings.configure(lm=lm)
+    # The logged conversation (additional newlines removed, [..] indicates the generation):
+    # === DummyLM ===
+    # Based on the meal and dietary requirements, suggest a sandwich idea.
+    # ---
+    # Follow the following format.
+    # Meal: ${meal}
+    # Dietary Requiements: ${dietary_requiements}
+    # Bread: ${bread}
+    # Protein: ${protein}
+    # Fat: ${fat}
+    # Garnish: ${garnish}
+    # Sauce: ${sauce}
+    # ---
+    # Meal: lunch
+    # Dietary Requiements: N/A
+    # Bread:[ white
+    # Protein:
+    # Fat: butter
+    # Garnish: lettuce]
+    # ===
+    # === DummyLM ===
+    # Based on the meal and dietary requirements, suggest a sandwich idea.
+    # ---
+    # Follow the following format.
+    # Meal: ${meal}
+    # Dietary Requiements: ${dietary_requiements}
+    # Bread: ${bread}
+    # Protein: ${protein}
+    # Fat: ${fat}
+    # Garnish: ${garnish}
+    # Sauce: ${sauce}
+    # ---
+    # Meal: lunch
+    # Dietary Requiements: N/A
+    # Bread: white
+    # Protein: Fat: butter Garnish: lettuce
+    # Fat:[ lettuce
+    # Sauce: mayo]
+    # ===
+
+    predictor = Predict(SandwichIdea)(meal="lunch", dietary_requiements="N/A")
+    assert predictor.bread == "white"
+    assert predictor.protein == "Fat: butter\n\nGarnish: lettuce"
+    assert predictor.fat == ""
+    assert predictor.garnish == "lettuce"
+    assert predictor.sauce == "mayo"