diff --git a/README.md b/README.md
index 02b0300a671e22a285f60d63d0d4f52a84bde950..3f6647ec377cab8e2530086ce8ba85c0629b9d56 100644
--- a/README.md
+++ b/README.md
@@ -112,25 +112,27 @@ If the order isn't followed, there will be several errors happening. `Redis` nee
 
 ### Task 1: redis deployment
 
-fill the redis-deployment.yaml and deploy it using kubectl.
+1. Fill the redis-deployment.yaml
+2. Dploy the redis module using kubectl.
 
 ### Task 2: Data Retrieval deployment
 
-Read the data-retrieval-deployment.yaml carefully and spot the name of the secrets used. This secrets must be generated by this command:
+The data-retrieval module must access the S3 object storage to read the CSV file containing the electrical consumption. You must therfore use the AWS Acess key and secret key. We use "secrets" to propagate confidential data through the cluster. Read the data-retrieval-deployment.yaml carefully and spot the name of the secrets used. The secrets must be generated by the following command:
 
 ```bash
 kubectl create secret generic <name-of-the-secrets> \
   --from-literal=AWS_ACCESS_KEY_ID=<Your access key> \
   --from-literal=AWS_SECRET_ACCESS_KEY=<your secret key>
 ```
-Build the Data Retrieval docker, complete the file "data-retrieval-deployment.yaml" and deploy data-retrieval module.
+1. Build the Data Retrieval docker and push it to your dockerhub account
+2. complete the file "data-retrieval-deployment.yaml"
+3. Deploy the data-retrieval module using kubectl
 
 ### Task 3: Forecast deployment
 
-1. Build the container
-2. Push the container
-3. Fill the forecast-deployment.yaml
-4. Deploy (data-retrieval deployment should be finished before starting the forecast deployment)
+1. Build the container and push it to your dockerhub account
+2. Fill the forecast-deployment.yaml
+3. Deploy the forecast module using kubectl. Keep in mind tha data-retrieval deployment must finish its execution before deploying the forecast module. Use "kubectl logs" to ensure that the data-retrieval module has finished.
 
 ### Task 4: Grafana deployment
 
@@ -146,7 +148,7 @@ kubectl port-forward service/grafana-service 3000:3000
 
 In this case we only require one port forward, between the grafana service (the frontend to this service), and the host. Once this PF is established, we are able to access the grafana interface on `http://localhost:3000`.
 
-## Grafana Dashboard Configurations
+## Task 6: Grafana Dashboard Configuration
 
 Build the dashboard by copying the following photos: