diff --git a/README.md b/README.md
index 28d5858a0..ed4489fbf 100644
--- a/README.md
+++ b/README.md
@@ -105,37 +105,65 @@ import asyncio
 from cognee.api.v1.search import SearchType
 
 async def main():
-    # Reset cognee data
+    # Create a clean slate for cognee -- reset data and system state
+    print("Resetting cognee data...")
     await cognee.prune.prune_data()
-    # Reset cognee system state
     await cognee.prune.prune_system(metadata=True)
+    print("Data reset complete.\n")
 
+    # cognee knowledge graph will be created based on this text
     text = """
     Natural language processing (NLP) is an interdisciplinary
     subfield of computer science and information retrieval.
     """
-
-    # Add text to cognee
+    
+    print("Adding text to cognee:")
+    print(text.strip())  
+    # Add the text, and make it available for cognify
     await cognee.add(text)
+    print("Text added successfully.\n")
 
+    
+    print("Running cognify to create knowledge graph...\n")
+    print("Cognify process steps:")
+    print("1. Classifying the document: Determining the type and category of the input text.")
+    print("2. Checking permissions: Ensuring the user has the necessary rights to process the text.")
+    print("3. Extracting text chunks: Breaking down the text into sentences or phrases for analysis.")
+    print("4. Adding data points: Storing the extracted chunks for processing.")
+    print("5. Generating knowledge graph: Extracting entities and relationships to form a knowledge graph.")
+    print("6. Summarizing text: Creating concise summaries of the content for quick insights.\n")
+    
     # Use LLMs and cognee to create knowledge graph
     await cognee.cognify()
+    print("Cognify process complete.\n")
 
-    # Search cognee for insights
+    
+    query_text = 'Tell me about NLP'
+    print(f"Searching cognee for insights with query: '{query_text}'")
+    # Query cognee for insights on the added text
     search_results = await cognee.search(
-        SearchType.INSIGHTS,
-        "Tell me about NLP",
+        SearchType.INSIGHTS, query_text=query_text
     )
-
+    
+    print("Search results:")
     # Display results
     for result_text in search_results:
         print(result_text)
-        # natural_language_processing is_a field
-        # natural_language_processing is_subfield_of computer_science
-        # natural_language_processing is_subfield_of information_retrieval
 
-asyncio.run(main())
+    # Example output:
+       # ({'id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'natural language processing', 'description': 'An interdisciplinary subfield of computer science and information retrieval.'}, {'relationship_name': 'is_a_subfield_of', 'source_node_id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'target_node_id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 15, 473137, tzinfo=datetime.timezone.utc)}, {'id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'computer science', 'description': 'The study of computation and information processing.'})
+       # (...)
+        #
+        # It represents nodes and relationships in the knowledge graph:
+        # - The first element is the source node (e.g., 'natural language processing').
+        # - The second element is the relationship between nodes (e.g., 'is_a_subfield_of').
+        # - The third element is the target node (e.g., 'computer science').
+
+if __name__ == '__main__':
+    asyncio.run(main())
+
 ```
+When you run this script, you will see step-by-step messages in the console that help you trace the execution flow and understand what the script is doing at each stage.
 A version of this example is here: `examples/python/simple_example.py`
 
 ### Create your own memory store
diff --git a/examples/python/simple_example.py b/examples/python/simple_example.py
index 47940ca6e..55b07c4c3 100644
--- a/examples/python/simple_example.py
+++ b/examples/python/simple_example.py
@@ -1,5 +1,4 @@
 import asyncio
-
 import cognee
 from cognee.api.v1.search import SearchType
 
@@ -11,29 +10,57 @@ from cognee.api.v1.search import SearchType
 
 async def main():
     # Create a clean slate for cognee -- reset data and system state
+    print("Resetting cognee data...")
     await cognee.prune.prune_data()
     await cognee.prune.prune_system(metadata=True)
+    print("Data reset complete.\n")
 
     # cognee knowledge graph will be created based on this text
     text = """
     Natural language processing (NLP) is an interdisciplinary
     subfield of computer science and information retrieval.
     """
-
+    
+    print("Adding text to cognee:")
+    print(text.strip())  
     # Add the text, and make it available for cognify
     await cognee.add(text)
+    print("Text added successfully.\n")
 
+    
+    print("Running cognify to create knowledge graph...\n")
+    print("Cognify process steps:")
+    print("1. Classifying the document: Determining the type and category of the input text.")
+    print("2. Checking permissions: Ensuring the user has the necessary rights to process the text.")
+    print("3. Extracting text chunks: Breaking down the text into sentences or phrases for analysis.")
+    print("4. Adding data points: Storing the extracted chunks for processing.")
+    print("5. Generating knowledge graph: Extracting entities and relationships to form a knowledge graph.")
+    print("6. Summarizing text: Creating concise summaries of the content for quick insights.\n")
+    
     # Use LLMs and cognee to create knowledge graph
     await cognee.cognify()
+    print("Cognify process complete.\n")
 
+    
+    query_text = 'Tell me about NLP'
+    print(f"Searching cognee for insights with query: '{query_text}'")
     # Query cognee for insights on the added text
     search_results = await cognee.search(
-        SearchType.INSIGHTS, query_text='Tell me about NLP'
+        SearchType.INSIGHTS, query_text=query_text
     )
-
-    # Display search results
+    
+    print("Search results:")
+    # Display results
     for result_text in search_results:
         print(result_text)
 
+    # Example output:
+       # ({'id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'natural language processing', 'description': 'An interdisciplinary subfield of computer science and information retrieval.'}, {'relationship_name': 'is_a_subfield_of', 'source_node_id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'target_node_id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 15, 473137, tzinfo=datetime.timezone.utc)}, {'id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'computer science', 'description': 'The study of computation and information processing.'})
+       # (...)
+        # It represents nodes and relationships in the knowledge graph:
+        # - The first element is the source node (e.g., 'natural language processing').
+        # - The second element is the relationship between nodes (e.g., 'is_a_subfield_of').
+        # - The third element is the target node (e.g., 'computer science').
+
 if __name__ == '__main__':
     asyncio.run(main())