diff --git a/data-fundamentals-dev-rel/connect-to-env/connect-to-env.md b/data-fundamentals-dev-rel/connect-to-env/connect-to-env.md
index 4e0015920..29e9bd6c7 100644
--- a/data-fundamentals-dev-rel/connect-to-env/connect-to-env.md
+++ b/data-fundamentals-dev-rel/connect-to-env/connect-to-env.md
@@ -19,7 +19,7 @@ Estimated Time: 5 minutes
 
 2. Paste in the Development IDE Login Password that you copied in the previous step. Click **Login**.
 
-    ![Login](./images/jupyter-login.png " ")
+    ![JupyterLab server login](./images/jupyter-login.png " ")
 
 1. Select **`notebooks/data_fundamentals`** directory to open it.  Double click on file **`data_fundamentals_lab.ipynb`** and it will open in the the panel on the right.
 
@@ -39,7 +39,7 @@ You will use a Jupyter Notebook in a JupyterLab server to build and test databas
 
     **NOTE:** Look for **<span style="color: green;">green text</span>** as in the image below where it says "<span style="color: green;">Connected successfully!</span>". Many cells will have different message, but the final successful one should always be green. When you see the green text, the cell completed. For some longer running cells, this is important to watch for.
 
-    ![JupyterLab blocks](./images/block.png " ")
+    ![JupyterLab cell example](./images/block.png " ")
 
 ## Task 3: Hybrid Vector Search lab section. **(Optional)** 
 
@@ -55,5 +55,4 @@ Make sure you take the quiz by clicking on **Take the quiz!** link on the left n
 
 ## Acknowledgements
 * **Authors** - Kirk Kirkconnell
-* **Contributors** - Anant Srivastava
 * **Last Updated By/Date** - Kirk Kirkconnell, June 2026
diff --git a/data-fundamentals-dev-rel/introduction/introduction.md b/data-fundamentals-dev-rel/introduction/introduction.md
index bdba1e46b..864898f52 100644
--- a/data-fundamentals-dev-rel/introduction/introduction.md
+++ b/data-fundamentals-dev-rel/introduction/introduction.md
@@ -6,6 +6,8 @@ In this lab, we'll interact with a data set from an application named Prism City
 
 You'll utilize a Jupyter Notebook with real Python code, completing real activities, and using aspects of Unified Model Theory (UMT) to present data in various forms, from JSON documents, to graph traversals with visual of those relationships, and perform AI search via vector data, all within Oracle AI Database.
 
+Estimated Workshop Time: 50 - 60 minutes
+
 ✅ **Overview of Labs**
 
 In the next labs, you'll connect to your JupyterLab environment and work through a data fundamentals notebook built around the Prism CityOps dataset. You'll inspect the same operational data through relational tables, JSON, graph, and vector representations, then use Oracle AI Database to generate embeddings, run Vector Search, and compare search patterns. Labs 1 and 2 will be in the morning session, and Labs 3 and 4 will be completed in the afternoon sessions.
@@ -48,5 +50,4 @@ This lab assumes you have:
 
 ## Acknowledgements
 * **Authors** - Kirk Kirkconnell
-* **Contributors** - Anant Srivastava
 * **Last Updated By/Date** - Kirk Kirkconnell, June 2026
diff --git a/data-fundamentals-dev-rel/workshops/sandbox/manifest.json b/data-fundamentals-dev-rel/workshops/sandbox/manifest.json
index f90337541..1def72137 100644
--- a/data-fundamentals-dev-rel/workshops/sandbox/manifest.json
+++ b/data-fundamentals-dev-rel/workshops/sandbox/manifest.json
@@ -16,6 +16,11 @@
             "title": "Take the quiz!",
             "description": "This is a step-by-step guide showcasing how the hands-on lab instance is navigated",
             "filename": "../../quiz/quiz.md"
+        },
+        {
+            "title": "Need Help?",
+            "description": "Solutions to Common Problems and Directions for Receiving Live Help",
+            "filename": "https://livelabs.oracle.com/cdn/common/labs/need-help/need-help-freetier.md"
         }
     ]
 }
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