Merge pull request #53994 from weslbo/refresh-modules

Refresh Fabric Copilot modules

Author: ttorble

learn-pr/wwl/get-started-copilot-fabric-data-engineering/data-pipelines.yml

@@ -4,7 +4,7 @@ title: Use Copilot with Microsoft Fabric data pipelines
 metadata:
   title: Use Copilot With Microsoft Fabric Data Pipelines
   description: "Use Copilot with Microsoft Fabric data pipelines"
-  ms.date: 08/18/2025
+  ms.date: 03/27/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl/get-started-copilot-fabric-data-engineering/dataflow-gen2.yml

@@ -4,7 +4,7 @@ title: Use Copilot with Microsoft Fabric Dataflow Gen2
 metadata:
   title: Use Copilot With Microsoft Fabric Dataflow Gen2
   description: "Use Copilot with Microsoft Fabric Dataflow Gen2"
-  ms.date: 08/18/2025
+  ms.date: 03/27/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl/get-started-copilot-fabric-data-engineering/exercise-copilot-fabric-dataflow-gen2.yml

@@ -4,7 +4,7 @@ title: Exercise - Work smarter with Copilot in Microsoft Fabric Dataflow Gen2
 metadata:
   title: Exercise - Work Smarter With Copilot in Microsoft Fabric Dataflow Gen2
   description: "Exercise - Work smarter with Copilot in Microsoft Fabric Dataflow Gen2"
-  ms.date: 08/18/2025
+  ms.date: 03/27/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl/get-started-copilot-fabric-data-engineering/exercise-copilot-fabric-notebooks.yml

@@ -4,7 +4,7 @@ title: Exercise - Analyze data with Apache Spark and Copilot in Microsoft Fabric
 metadata:
   title: Exercise - Analyze Data With Apache Spark and Copilot in Microsoft Fabric Notebooks
   description: "Exercise - Analyze data with Apache Spark and Copilot in Microsoft Fabric notebooks"
-  ms.date: 08/18/2025
+  ms.date: 03/27/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl/get-started-copilot-fabric-data-engineering/includes/data-pipelines.md

@@ -55,4 +55,24 @@ As an example, consider the following prompt:
 Summarize this pipeline, turning the technical configuration into a conceptual narrative.
 ```
 
-Copilot supports the **how** of building a pipeline: it scaffolds connections, ingestion, and transformation activities using natural language. It also supports the **why**: encouraging you to validate, configure, and reflect on each stage, building both confidence and transferable data engineering skills.
+## Troubleshoot with the error message assistant
+
+When a pipeline run fails, Copilot can analyze the error and suggest actionable remediation steps in plain language. This removes the need to parse raw error logs manually and helps you resolve issues in context. As an example:
+
+```copilot-prompt
+Why did this pipeline fail?
+```
+
+Copilot explains the cause of the failure and recommends specific steps to fix it — bridging the gap between a technical error and a meaningful resolution.
+
+## Build pipeline expressions
+
+Pipelines often require dynamic expressions for things like constructing file paths, formatting dates, or conditional logic. Copilot integrates with the **Expression Builder** to generate and explain these expressions using natural language.
+
+```copilot-prompt
+Create an expression that builds a dynamic output file path using today's date in yyyy-MM-dd format.
+```
+
+Beyond generation, you can also ask Copilot to explain what an existing expression does — helping you learn the expression language incrementally without leaving the pipeline editor.
+
+Copilot supports the **how** of building a pipeline: it scaffolds connections, ingestion, transformation activities, error troubleshooting, and expressions using natural language. It also supports the **why**: encouraging you to validate, configure, and reflect on each stage, building both confidence and transferable data engineering skills.

learn-pr/wwl/get-started-copilot-fabric-data-engineering/includes/dataflow-gen2.md

@@ -11,7 +11,7 @@ Think of the process as a cycle of **prompt → output → validation → refine
 > [!div class="mx-imgBorder"]
 > [![Diagram showing the process as a cycle.](../media/prompt-cycle.png)](../media/prompt-cycle.png#lightbox)
 
-**Data ingestion** is the starting point. Without data, there is nothing to transform. You might begin by generating sample records to experiment with transformations. Currently Copilot can generate sample tables, but initiating ingestion directly from an external data source through a prompt is not yet supported. Here's an example prompt:
+**Data ingestion** is the starting point. Without data, there is nothing to transform. You might begin by generating sample records to experiment with transformations. Copilot can generate sample tables directly, and as a Preview capability, Copilot in the **Get Data** wizard now also supports ingesting recently used tables using natural language — you can describe filters and transformations before loading the data. Here's an example prompt for generating sample data:
 
 ```copilot-prompt
 Create a new query with 50 patient records including patient-id, age, gender, and satisfaction-score.
@@ -65,9 +65,15 @@ The VisitDateTime field didn’t parse correctly—recreate it using the format
 
 Copilot not only executes transformations; it also explains the underlying Mashup (M) code. This transparency builds confidence by showing what happens under the hood, offers a gradual path to learn query syntax while still working in natural language, and promotes reflection as you compare your intent with the logic it generates.
 
-```copilot-prompt
-Describe this query
-```
+There are two levels of explanation available:
+
+- **Explain this query**: Get a plain-language description of the full query, including all applied steps. Trigger this from the Copilot pane or by right-clicking a query in the Queries pane and selecting **Describe**.
+
+  ```copilot-prompt
+  Describe this query
+  ```
+
+- **Explain this step**: Get an explanation for a single transformation step. Right-click any step in the **Applied Steps** list and select **Explain**. This is particularly useful when you want to understand one transformation in isolation rather than parsing the entire M expression at once.
 
 ## Best practices for working with Copilot for Dataflow Gen2
 

learn-pr/wwl/get-started-copilot-fabric-data-engineering/includes/notebooks.md

@@ -3,7 +3,8 @@ Copilot for Fabric Data Engineering assists with writing code in notebooks. It w
 There are multiple ways to interact with Copilot for Fabric Data Engineering, including:
 
 - **Magic commands** are special commands that you can use in your notebook cells to perform specific tasks. They start with a double percent sign (`%%`) or a single percent sign (`%`).
-- **Copilot chat panel**: You can ask Copilot for help in plain English, and it generates code for you based on your request.
+- **Copilot chat pane**: You can ask Copilot for help in plain English, and it generates code for you based on your request. This is best for multi-step workflows, building logic across cells, and reviewing generated code with diff view.
+- **In-cell Copilot**: You can interact with Copilot directly above any individual code cell using a text box and slash commands. This is best for focused, single-cell actions like fixing errors or explaining logic.
 
 ## Magic commands
 
@@ -19,7 +20,22 @@ Chat commands are special commands you can use in your Microsoft Fabric notebook
 | `%describe` | Provides summaries and descriptions of loaded dataframes. |
 | `%%add_comments` | Add comments to your code. |
 
-In addition, there are also several commands allowing you to fix errors, configure privacy settings, and manage dataframes. Check out the article [Overview of chat-magics in Microsoft Fabric notebooks](/fabric/data-engineering/copilot-notebooks-chat-magics) for more information.
+In addition, there are also several commands allowing you to fix errors, configure privacy settings, and manage dataframes. See [Use the Copilot chat pane in Microsoft Fabric notebooks](/fabric/data-engineering/copilot-notebooks-chat-pane) for more information.
+
+## In-cell Copilot
+
+In-cell Copilot lets you interact with Copilot directly above any code cell, without leaving your editing context. Select the **Copilot** button that appears above a code cell to open a text box where you can type a request or use a slash command.
+
+The available slash commands are:
+
+| Command | Description |
+| ------- | ----------- |
+| `/explain` | Provides a plain-language explanation of the cell's code logic. |
+| `/fix` | Identifies errors in the cell and suggests corrections. |
+| `/comments` | Automatically adds code comments to document logic and data changes. |
+| `/optimize` | Suggests improvements for performance and efficiency. |
+
+Use in-cell Copilot when you want to refine a specific cell — for example, understanding what a transformation does, fixing a syntax error, or documenting code before sharing the notebook. For multi-step workflows or cross-cell tasks, switch to the Copilot chat pane instead.
 
 ## Example
 

learn-pr/wwl/get-started-copilot-fabric-data-engineering/index.yml

@@ -3,7 +3,7 @@ uid: learn.wwl.get-started-copilot-fabric-data-engineering
 metadata:
   title: Get Started With Copilot in Microsoft Fabric for Data Engineering
   description: Get Started with Copilot in Microsoft Fabric for data engineering.
-  ms.date: 08/18/2025
+  ms.date: 03/27/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: module

learn-pr/wwl/get-started-copilot-fabric-data-engineering/introduction.yml

@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction"
-  ms.date: 08/18/2025
+  ms.date: 03/27/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit

learn-pr/wwl/get-started-copilot-fabric-data-engineering/knowledge-check.yml

@@ -4,7 +4,7 @@ title: Module assessment
 metadata:
   title: Module Assessment
   description: "Knowledge check"
-  ms.date: 08/18/2025
+  ms.date: 03/27/2026
   author: weslbo
   ms.author: wedebols
   ms.topic: unit