review lab content

content/modules/ROOT/pages/01-02-current-process.adoc

@@ -7,7 +7,7 @@ include::_attributes.adoc[]
 ** fax,
 ** phone,
 ** web form.
-* All formats will have to be transcribed into the web form for better uniformity.
+* All formats have to be transcribed into the web form for better uniformity and processing.
 * Claims have to be processed by (human) claims adjusters.
 ** They spend an aggregated average of 7 hours/claim.
 ** We estimate that human errors account for $2.5M/year in lost revenue.

content/modules/ROOT/pages/01-05-results.adoc

@@ -12,6 +12,6 @@ include::_attributes.adoc[]
 
 We know that you all have various background and responsibilities (Coders, IT, DevOps, Data Scientists, etc..).
 
-But before you start working in your narrow area of specialization, we want you all to have a decent understanding of all the work that was done during the prototype phase.
+So before you start working in your narrow area of specialization, we want you all to have a decent understanding of all the work that was done during the prototype phase.
 
 The next 75 minutes is your official training session, for you to ramp up and gain an understanding of the various technologies involved during the prototype phase.

content/modules/ROOT/pages/02-01-getting-connected.adoc

@@ -7,7 +7,7 @@ Each person attending this lab will have a unique user account in which to do th
 
 == Environment information
 
-If you are using the customized version of the instructions, the information below will render properly. If not, you will see placeholder values instead.
+__Note: if you are using the lab-deployed instructions, the values below will render properly. If not, like when using the rendered instructions from the Github repo, placeholder values will appear.__
 
 * Your account id: `{user}`
 * Your password: `{password}`
@@ -19,12 +19,12 @@ In a new window or tab, open the following URL and log in:
 * Click on the `Login with OpenShift` button:
 +
 [.bordershadow]
-image::02/02-01-login3.png[width=50%]
+image::02/02-01-login3.png[]
 * Enter your credentials (as detailed above)
 * The result should look like:
 +
 [.bordershadow]
-image::02/02-01-login1.png[width=50%]
+image::02/02-01-login1.png[]
 
 * Because the password is so simple (`{password}`), your browser might display a scary message such as:
 +

content/modules/ROOT/pages/02-02-auto-created-project.adoc

@@ -13,14 +13,19 @@ In order to give you a feel for what "day 2" experience would be like, we have p
 image::02/02-02-ds-proj-nav.png[]
 
 * Then, open the project called {user}.
++
+[.bordershadow]
+image::02/02-02-open-project.png[]
 
-* Inside the project you should see a few items that already have been pre-created:
+* Inside the project, by clicking on the different tabs, you should see a few items that already have been pre-created or configured for you:
 +
 [.bordershadow]
 image::02/02-02-pre-created-components.png[]
 
 * These components are as follows:
-1. A Workbench - this is your environment that you can experiment and train in.
-2. A Cluster storage - this is a persistent storage for your workbench.
-3. A Data Connection - it contains all the information needed to talk with an existing S3 storage, we use that to store models and pipeline artifacts.
-4. A Pipeline Server - the pipeline server has already been set up here so you can import or run data science pipelines right away.
+1. Workbenches: a workbench has already been created - this is the environment that you can experiment and train models in.
+2. Pipelines: a pipeline server server has already been configured so you can import or run data science pipelines right away. At the moment, it is empty, but we will populate it with a pipeline in a next section.
+3. Models: the models panel is empty at the moment.
+4. Cluster storage: this is where you find a persistent storage for your workbench and one for the pipeline we will create later.
+5. Data Connections: it contains all the information needed to connect with an existing S3 storage. We use it to store models and pipeline artifacts.
+6. Permissions: this is where you can manage the permissions for the project.

content/modules/ROOT/pages/02-03-auto-created-workbench.adoc

@@ -1,6 +1,8 @@
 = Pre-Created workbench
 include::_attributes.adoc[]
 
+Please head to the Workbenches tab.
+
 One of the components that was pre-created for you is the workbench.
 
 * If you click on the arrow next to the workbench, you can see some of the libraries included in it as well as how much resources it has been given.

content/modules/ROOT/pages/03-06-confidence-check.adoc

@@ -51,23 +51,33 @@ image::03/03-06-unsaved-changes.png[]
 image::03/03-07-run-pipeline-ok.png[]
 
 . When you get a popup that says **Job submission to Data Science Pipelines succeeded**, click the link **Run details** to see how the pipeline is progressing.
++
+[.bordershadow]
+image::03/03-06-running-pipeline.png[]
 
 == Schedule execution
 
 We can also **schedule** an execution so that the confidence check is executed at regular intervals. +
 To do that:
 
 . Go back to the {rhoai} Data Science Project
-. Find the pipeline you just ran
-. Click the 3 dots at the very end of the line, and click "Create run".
+. Find the pipeline you just ran in the Pipelines tab
+. Click the 3 dots at the very end of the line, and click "Create schedule".
 +
 [.bordershadow]
-image::03/07-create-run.png[create run]
-
-. On the next screen, choose a name, select a Periodic run to trigger every **Day** and click **Create**:
+image::03/07-create-schedule.png[create schedule]
+
+. On the next screen:
+    - keep the **Experiment** to `Default`,
+    - choose a **Name**,
+    - select a `Periodic` **Trigger type**,
+    - run it every **Day**,
+    - keep the `confidence-pipeline` **Pipeline** and **Version**,
+    - and click **Create**:
 +
 [.bordershadow]
 image::03/03-06-dailyrun.png[]
 
+
 . We can now leave the confidence-check pipeline alone.
 . It will run daily, and will inform us if anything goes wrong with our LLM.

content/modules/ROOT/pages/04-05-model-serving.adoc

@@ -8,7 +8,11 @@ include::_attributes.adoc[]
 
 == Create a Data Connection
 
-* In your Data Science project, create a Data Connection that refers to the shared MinIO.
+* In your Data Science project, on the Data Connection tab, click on **Add data connection** to create one that refers to the shared storage (Minio) where a copy of the model is stored.
++
+[.bordershadow]
+image::04/04-add-data-connection.png[]
+
 * Here is the info you need to enter:
 ** Name:
 [.lines_space]
@@ -54,9 +58,11 @@ image::04/model-data-connection.png[model connection]
 
 == Create a Model Server
 
-In your project create a model server:
+In your project, select the **Models** tab to create a model server:
 
-* In the "Multi-model serving platform" type of model, click **Add model server**:
+**VERY IMPORTANT**: in the following step, pay attention and select the right type of model server. Otherwise we would have to reset your project.
+
+* In the **Multi-model serving platform** type of model, the one on the right, click **Add model server**:
 +
 [.bordershadow]
 image::04/add-model-server.png[]
@@ -92,7 +98,7 @@ Standard
 [.console-input]
 [source, text]
 [subs=attributes+]
-None
+None (bug: even when selecting `None`, the drop down will still show `Select...`)
 ** Model route
 [.lines_space]
 [.console-input]
@@ -116,7 +122,7 @@ image::04/add-model-server-config.png[]
 
 == Deploy the Model
 
-In your project, under **Models and model servers** select **Deploy model**.
+Still in your project, on the **Models** tab, under **Models and model servers**:
 
 * Click **Deploy model**:
 +
@@ -136,7 +142,7 @@ My first Model
 [.console-input]
 [source, text]
 [subs=attributes+]
-My first Model Server
+My first Model Server (pre-filled)
 ** Model server - Model framework
 [.lines_space]
 [.console-input]

content/modules/ROOT/pages/05-01-application.adoc

@@ -14,7 +14,7 @@ The different components are:
 - The **frontend**: the application itself, developed using the Patternfly framework (React, Typescript), running in the browser of the user.
 - The **backend**: a Python FastAPI application, running in a container on {ocp}. It handles the communication with the database, the LLM, and the Object storage. It handles the communication with the frontend by exposing a REST API.
 - The **database**: a PostgreSQL database, running in a container on {ocp}. It stores the claims.
-- The **LLM**: the language model used to summarize and extract information from the claims. It is consumed by the backend through its API.
+- The **LLM**: the language model used to summarize and extract information from the claims, and powering the chatbot. It is consumed by the backend through its API.
 - The **VectorDB**: the vector database used to store documents for retrieval in the RAG part. It's based on Milvus.
 - The **Object storage**: an S3-compatible object storage. It stores the claim images.
 
@@ -28,21 +28,22 @@ If you want to have a look at the code of the application, you can do it directl
 
 +
 [.bordershadow]
-image::05/05-toggle-off-workbench.jpg[]
-
+image::05/05-toggle-off-workbench.png[]
+[.bordershadow]
+image::05/05-confirm-turn-off.png[]
 
 
--- You should probably give it 5 or 10 seconds to fully stop.
+- You should probably give it 5 or 10 seconds to fully stop.
 - Edit the Workbench configuration by clicking the 3 vertical dots on the right, then choose the **Edit workbench** button.
 +
 [.bordershadow]
 image::05/stop-workbench.png[]
 
-- In the image section, change the image to `CUSTOM - VSCode for {ic}`:
+
+- In the `image selection` section, change the image to `CUSTOM - Code Server for {ic}`:
 +
 [.bordershadow]
-image::05/05-change-to-vscode.jpg[]
-- Note that this is a Custom-built image added in this particular {ic-lab} to illustrate the flexibility of the {rhoai} platform. Not everyone wants to use Jupyter Notebooks for everything.
+image::05/05-change-to-vscode.png[]
 
 - Then click on `Update workbench` at the bottom of the page:
 +
@@ -51,7 +52,7 @@ image::05/update-workbench.png[]
 
 - You can now start your Workbench again by using the toggle.
 - Once the Workbench is started, click on the **Open** Link.
-- You will be redirected to the VSCode IDE.
+- You will be redirected to the VSCode IDE (Code Server is the open source implementation of VSCode).
 - After logging in and accepting the authorization, VSCode will open.
 - The first time you connect to it, you must indicate that you trust the workspace.
 - Click on the **Yes, I trust the authors** button.
@@ -63,7 +64,7 @@ image::05/trust-vscode.png[]
 * Your Persistent Volume has been automatically reconnected to this new environment.
 ** Therefore, all the data you created in the Jupyter environment is still available
 ** You can see more files because VSCode displays hidden files by default, which Jupyter does not do.
-* If you want, you can now explore the code of the application, in the `app` folder:
+* If you want, you can now explore the code of the application, in the `parasol-insurance/app` folder:
 +
 [.bordershadow]
 image::05/app-code.png[]

content/modules/ROOT/pages/05-03-web-app-deploy-application.adoc

@@ -14,7 +14,7 @@ image::05/05-open-argocd.png[]
 +
 [.bordershadow]
 image::05/05-login-argocd-openshift.png[]
-
++
 [.bordershadow]
 image::05/05-openshift-login.png[]
 
@@ -28,7 +28,7 @@ image::05/05-create-application.png[]
 [.bordershadow]
 image::05/05-edit-as-yaml.png[]
 
-- Copy the content of the following text, paste it in the editor, then click on "Save".
+- Copy the content of the following text, paste it in the editor (replace the default content), then click on "Save".
 +
 [source, yaml, subs="attributes+"]
 apiVersion: argoproj.io/v1alpha1
@@ -49,7 +49,7 @@ spec:
       prune: true
       selfHeal: false
     syncOptions: [CreateNamespace=false]
-
++
 [.bordershadow]
 image::05/05-application-yaml-save.png[]
 

content/modules/ROOT/pages/05-04-web-app-validating.adoc

@@ -5,42 +5,44 @@ include::_attributes.adoc[]
 
 What you see when you first connect to the application is the new version.
 
-But to see where we are coming from, let's start by navigating to the original version and see what it looked like. Click on the **Original App** menu on the bottom left.
+But to see where we are coming from, let's start by navigating to the original version and see what it looked like.
 
+- Click on the **Original App** menu on the bottom left.
++
 [.bordershadow]
 image::05/original-app-link.png[]
 
-The Original App gives a view of what the older application used at {company-name} looks like. You can navigate each claim (click on tabs 'Claim 1', 'Claim 2' and 'Claim 3') and see that the interface was very crude, with a simple copy of the originally received email with the pictures that were attached to it.
-
+- The Original App gives a view of what the older application used at {company-name} looks like. You can navigate each claim (click on tabs 'Claim 1', 'Claim 2' and 'Claim 3') and see that the interface was very crude, with a simple copy of the originally received email with the pictures that were attached to it.
++
 [.bordershadow]
 image::05/original-app-1.png[]
 
 == Prototype App
 
 The Prototype App is the first version of the new application that we built to show to the board. Of course, not all features are implemented in this prototype, but it was enough to show the potential of the new application to help with claim processing.
 
-Click on the third item from menu on the left, Claims.
-
+- Click on the third item from menu on the left, Claims.
++
 [.bordershadow]
 image::05/claims-link.png[]
 
-You now have a list of all the claims that have been received by the application. You can navigate each claim (click the subject line) and see that the interface is much more user friendly, with a nice interface to navigate the claim and see the pictures that were attached to it.
-
+- You now have a list of all the claims that have been received by the application. You can navigate each claim (click the subject line) and see that the interface is much more user friendly, with a nice interface to navigate the claim and see the pictures that were attached to it.
++
 [.bordershadow]
 image::05/new-app-claims.png[]
 
-Moreover, for the claims that have been processed, you can see the summary of this claim as well as the customer sentiment analysis, the location and time of the event that have been extracted from the claim, and the pictures that have been processed by the AI.
-
+- Moreover, for the claims that have been processed, you can see the summary of this claim as well as the customer sentiment analysis, the location and time of the event that have been extracted from the claim, and the pictures that have been processed by the AI.
++
 [.bordershadow]
 image::05/new-app-1.png[]
 
-You also now have access to an assistant that can help you with the claim processing. Open the assistant by clicking on the 'Chat icon' at the bottom right of an opened claim.
-
+- You also now have access to an assistant that can help you with the claim processing. Open the assistant by clicking on the 'Chat icon' at the bottom right of an opened claim.
++
 [.bordershadow]
 image::05/new-app-chat-icon.png[]
 
-The assistant will open and you can ask it questions about the claim. As we have seen in the RAG notebook, the assistant can rely on some documentation to answer your questions. For example, on claim **CLM202401** Try with: "Did Daniel have the right to pass at the red light?".
-
+- The assistant will open and you can ask it questions about the claim. As we have seen in the RAG notebook, the assistant can rely on some documentation to answer your questions. For example, on claim **CLM202401** Try with: "Did Daniel have the right to pass at the red light?".
++
 [.bordershadow]
 image::05/new-app-chat.png[]