remove superfluous/LLM verbosity

Signed-off-by: Aydin Abiar <[email protected]>

Author: Aydin Abiar

doc/source/data/examples/unstructured-data-ingestion/content/README.md

@@ -843,7 +843,7 @@ quality_distribution.write_parquet(
 )
 ```
 
-## Verification and Summary
+## Verification
 
 After writing data to the warehouse, verify everything worked correctly. This section demonstrates:
 
@@ -919,7 +919,7 @@ for i, record in enumerate(samples):
     print(f"\t{i+1}. Doc: {doc_id}, Category: {category}, Words: {words}, Quality: {quality}")
 ```
 
-## Summary and Next Steps
+## Summary
 
 You have built a complete end-to-end document ingestion pipeline using Ray Data. This section reviews what you learned and where to go from here.
 
@@ -1071,28 +1071,3 @@ This pipeline demonstrated all major Ray Data operations:
 - Monitoring and debugging
 - Scalability considerations
 
-### Next Steps
-
-**Extend This Pipeline:**
-1. Add LLM-based content analysis (replace pattern matching)
-2. Implement named entity recognition (NER)
-3. Add sentiment analysis for customer documents
-4. Create vector embeddings for semantic search
-5. Integrate with Delta Lake or Apache Iceberg
-
-**Learn More Ray Data:**
-- **Batch Inference**: Process documents with ML models
-- **Data Quality**: Advanced validation patterns
-- **Performance Tuning**: Optimize for your workload
-- **Integration**: Connect to Snowflake, Databricks, etc.
-
-### Resources
-
-- **Ray Data Documentation**: https://docs.ray.io/en/latest/data/data.html
-- **Ray Data Examples**: https://docs.ray.io/en/latest/data/examples/examples.html
-- **Ray Dashboard Guide**: https://docs.ray.io/en/latest/ray-observability/getting-started.html
-- **Anyscale Platform**: https://docs.anyscale.com/
-
----
-
-**You're now ready to build production-scale document ingestion pipelines with Ray Data!**

doc/source/data/examples/unstructured-data-ingestion/content/unstructured-data-ingestion.ipynb

@@ -1001,7 +1001,7 @@
    "id": "6de9c8b7",
    "metadata": {},
    "source": [
-    "## Verification and Summary\n",
+    "## Verification\n",
     "\n",
     "After writing data to the warehouse, verify everything worked correctly. This section demonstrates:\n",
     "\n",
@@ -1093,7 +1093,7 @@
    "id": "81f2d389",
    "metadata": {},
    "source": [
-    "## Summary and Next Steps\n",
+    "## Summary\n",
     "\n",
     "You have built a complete end-to-end document ingestion pipeline using Ray Data. This section reviews what you learned and where to go from here.\n",
     "\n",
@@ -1243,33 +1243,7 @@
     "- Error handling approaches\n",
     "- Resource optimization\n",
     "- Monitoring and debugging\n",
-    "- Scalability considerations\n",
-    "\n",
-    "### Next Steps\n",
-    "\n",
-    "**Extend This Pipeline:**\n",
-    "1. Add LLM-based content analysis (replace pattern matching)\n",
-    "2. Implement named entity recognition (NER)\n",
-    "3. Add sentiment analysis for customer documents\n",
-    "4. Create vector embeddings for semantic search\n",
-    "5. Integrate with Delta Lake or Apache Iceberg\n",
-    "\n",
-    "**Learn More Ray Data:**\n",
-    "- **Batch Inference**: Process documents with ML models\n",
-    "- **Data Quality**: Advanced validation patterns\n",
-    "- **Performance Tuning**: Optimize for your workload\n",
-    "- **Integration**: Connect to Snowflake, Databricks, etc.\n",
-    "\n",
-    "### Resources\n",
-    "\n",
-    "- **Ray Data Documentation**: https://docs.ray.io/en/latest/data/data.html\n",
-    "- **Ray Data Examples**: https://docs.ray.io/en/latest/data/examples/examples.html\n",
-    "- **Ray Dashboard Guide**: https://docs.ray.io/en/latest/ray-observability/getting-started.html\n",
-    "- **Anyscale Platform**: https://docs.anyscale.com/\n",
-    "\n",
-    "---\n",
-    "\n",
-    "**You're now ready to build production-scale document ingestion pipelines with Ray Data!**"
+    "- Scalability considerations\n"
    ]
   }
  ],