Update dbt source post review

docs/dagster-university/pages/dagster-dbt/lesson-3/3-representing-the-dbt-project-in-dagster.md

@@ -8,15 +8,17 @@ lesson: '3'
 
 As you’ll frequently point your Dagster code to the `target/manifest.json` file and your dbt project in this course, it’ll be helpful to keep a reusable representation of the dbt project. This can be easily done using the `DbtProject` class.
 
-Create a new file in the `assets` directory called `dbt.py`. Open that file and add the following import at the top:
+In the `assets` directory, create a new `dbt.py` file and add the following imports:
 
 ```python
 from pathlib import Path
 
 from dagster_dbt import DbtProject
 ```
 
-The `Path` class from the `pathlib` standard library will help us create an accurate pointer to where our dbt project is. The `DbtProject` class is imported from the `dagster_dbt` package that we installed earlier. After the import, add the following code: 
+The `Path` class from the `pathlib` standard library will help us create an accurate pointer to where our dbt project is. The `DbtProject` class is imported from the `dagster_dbt` package that we installed earlier. 
+
+After the import, add the following code: 
 
 ```python
 dbt_project = DbtProject(
@@ -26,7 +28,7 @@ dbt_project = DbtProject(
 
 This code creates a representation of the dbt project called `dbt_project`. The code defining the location of the project directory might look a little complicated, so let’s break it down:
 
-- It uses the location of the `dbt.py` file (via `__file__`) as a point of reference for finding the dbt project
+- The location of the `dbt.py` file (via `__file__`) is used as a point of reference for finding the dbt project
 - The arguments in `joinpath` point us towards our dbt project by appending the following to the current path:
    - Three directory levels up (`"..", "..", ".."`)
    - A directory named `analytics`, which is the directory containing our dbt project

docs/dagster-university/pages/dagster-dbt/lesson-3/5-loading-dbt-models-into-dagster-as-assets.md

@@ -28,7 +28,7 @@ We’ll only create one `@dbt_assets` definition for now, but in a later lesson,
 
 ## Loading the models as assets
 
-1. Navigate to the `assets/dbt.py` created earlier and open it.
+1. Open the `assets/dbt.py` file.
 
 2. Add the following imports to the top of the file:
 
@@ -37,13 +37,7 @@ We’ll only create one `@dbt_assets` definition for now, but in a later lesson,
    from dagster_dbt import dbt_assets, DbtCliResource
    ```
 
-3. The `@dbt_assets` decorator requires a path to the project’s manifest file, which is within our `dbt_project` representation created earlier. The manifest path can be easily accessed with the code below:
-
-   ```python
-   dbt_project.manifest_path
-   ```
-
-4. Now, use the `@dbt_assets` decorator to create a new asset function and provide it with a reference to the manifest:
+3. Next, we'll use the `@dbt_assets` decorator to create a new asset function and provide it with a reference to the project's manifest file:
 
    ```python
    @dbt_assets(
@@ -52,7 +46,9 @@ We’ll only create one `@dbt_assets` definition for now, but in a later lesson,
    def dbt_analytics(context: AssetExecutionContext, dbt: DbtCliResource):
    ```
 
-5. Finally, add the following to the body of `dbt_analytics` function:
+   Here, we used `dbt_project.manifest_path` to provide the reference to the project's manifest file. This is possible because the `dbt_project` representation we created earlier contains the manifest path, accessible by using the `manifest_path` attribute.
+
+4. Finally, add the following to the body of `dbt_analytics` function:
 
    ```python
    yield from dbt.cli(["run"], context=context).stream()

docs/dagster-university/pages/dagster-dbt/lesson-4/2-speeding-up-the-development-cycle.md

@@ -8,7 +8,7 @@ lesson: '4'
 
 By now, you’ve had to run `dbt parse` to create the manifest file and reload your code location quite frequently, which doesn’t feel like the cleanest developer experience.
 
-Before we move on, we’ll reduce the number of steps in the feedback loop. We'll automate the creation the manifest file by taking advantage of the `DbtProject` that we wrote earlier.
+Before we move on, we’ll reduce the number of steps in the feedback loop. We'll automate the creation of the manifest file by taking advantage of the `dbt_project` representation that we wrote earlier.
 
 ---
 
@@ -22,14 +22,14 @@ In `dbt.py`, after the code initializing `dbt_project`, add the following code:
 dbt_project.prepare_if_dev()
 ```
 
-If you look at the dbt project’s `/target` directory, you’ll see it stores the artifacts. When reloading your code in local development, when using `dagster_dev`, you'll see that a new manifest file is generated.
+If you look at the dbt project’s `/target` directory, you’ll see it stores the artifacts. When you use `dagster dev` in local development and you reload your code, you'll see that a new manifest file is generated.
 
 Reload your code location in the Dagster UI, and you’ll see that everything should still work: the dbt models are still shown as assets and you can manually materialize any of the models. The key difference is that you no longer have to manually run `dbt parse` anymore!
 
 ---
 
 ## Creating the manifest for production
 
-This is great, however, it only handles the preparation of a new manifest file in local development. In production, where a dbt project is stable, we may want to prepare a new manifest file only at build time, during the deployment process. This can be done using the command line interface (CLI) available in the `dagster_dbt` package installed earlier.
+This is great, however, it only handles the preparation of a new manifest file in local development. In production, where a dbt project is stable, we may want to prepare a new manifest file only at build time, during the deployment process. This can be done using the command line interface (CLI) available in the `dagster_dbt` package.
 
 Don't worry about the details for now! In Lesson 7, we’ll discuss the details on how to create a manifest file programmatically during deployment using the `dagster_dbt` CLI.

docs/dagster-university/pages/dagster-dbt/lesson-7/4-creating-the-manifest-during-deployment.md

@@ -12,7 +12,7 @@ Building your manifest for your production deployment will be needed for both op
 
 Since your CI/CD will be running in a fresh environment, you'll need to install dbt and other dependencies before building your manifest.
 
-To get our deployment working, we need to add a step to our GitHub Actions workflow that runs the commands required to generate the `manifest.json`. Specifically, we need to run the `dbt project prepare-and-package` command, available in the `dagster_dbt` package discussed earlier.
+To get our deployment working, we need to add a step to our GitHub Actions workflow that runs the commands required to generate the `manifest.json`. Specifically, we need to run the `dbt project prepare-and-package` command, available in the `dagster_dbt` package.
 
 1. In your Dagster project, locate the `.github/workflows` directory.
 2. Open the `deploy.yml` file.

docs/dagster-university/pages/dagster-dbt/lesson-7/5-preparing-for-a-successful-run.md

@@ -90,9 +90,9 @@ dbt_project = DbtProject(
 
 ## Adding a prod target to deploy.yml
 
-After that, we need to update the dbt commands in the `.github/workflows/deploy.yml` file to target the new `prod` profile. This will ensure that dbt uses the correct connection details when the GitHub Action runs as part of our Dagster+ deployment.
+Next, we need to update the dbt commands in the `.github/workflows/deploy.yml` file to target the new `prod` profile. This will ensure that dbt uses the correct connection details when the GitHub Action runs as part of our Dagster+ deployment.
 
-Open the file, scroll to the environment variable sections, and set an environment variable named `DBT_TARGET` to `prod`. This command should be on or around line 12:
+Open the file, scroll to the environment variable section, and set an environment variable named `DBT_TARGET` to `prod`. This should be on or around line 12:
 
 ```bash
 env: