diff --git a/doc/source/developer-guide.md b/doc/source/developer-guide.md
index cff16bb..04e6a72 100644
--- a/doc/source/developer-guide.md
+++ b/doc/source/developer-guide.md
@@ -9,9 +9,12 @@ Openstack Gyan is the Machine Learning Infra as a service project. This document
 # How to setup Gyan
 
   - Clone the devstack master branch 
-    > git clone https://github.com/openstack-dev/devstack
+    ```
+    git clone https://github.com/openstack-dev/devstack
+    ```
   - Copy the contents below to local.conf in devstack
-    > [[local|localrc]]
+    ```
+    [[local|localrc]]
     HOST_IP=192.168.1.188
     SERVICE_HOST=192.168.1.188
     DATABASE_PASSWORD=password
@@ -24,30 +27,41 @@ Openstack Gyan is the Machine Learning Infra as a service project. This document
     enable_plugin heat https://git.openstack.org/openstack/heat
     LIBS_FROM_GIT="python-gyanclient"
     ENABLED_SERVICES+=gyan-api
+    ```
   - Run stack.sh
-     > ./stack.sh
+    ```
+    ./stack.sh
+    ```
  
 # Workflow of Gyan:
   - Create hints.yaml to specify the size of the compute node. For example
-    > python_version: 2.7
+    ```
+      python_version: 2.7
       cpu: 2
       memory: 1024 Mb
       disk: 20 Gb
       driver: TensorflowDriver
       additional_details: {}
+    ```
   - After that create the flavor using hints template
-    > gyan flavor-create --hints-path flavor.yaml tensorflow
+    ```
+    gyan flavor-create --hints-path flavor.yaml tensorflow
+    ```
   - Compress your ML model that is already trained  and create a Gyan model
-    > gyan create-model --trained-model model.zip --type Tensorflow MNIST
+    ```
+    gyan create-model --trained-model model.zip --type Tensorflow MNIST
+    ```
   - Deploy model that was created in the previous step.
-    > gyan deploy-model <model-id>
-  - The above command will launch the compute node based on the flavor details we      gave in the first step. Once the compute node is launched, the gyan-compute will   be installed and connected to gyan-server.
+    ```
+    gyan deploy-model <model-id>
+    ```
+  - The above command will launch the compute node based on the flavor details we gave in the first step. Once the compute node is launched, the gyan-compute will   be installed and connected to gyan-server.
   - We should get new host in `gyan host-list`.
-  - In the last step you should see the deployed url of the model. We can find out     using `gyan model-list` 
+  - In the last step you should see the deployed url of the model. We can find out using `gyan model-list` 
 
 # How to use run sample example KnowThyNumber provided with Gyan:
   - Make sure you have installed golang in your system and set the GOROOT and GOPATH properly.
-  -  Copy the KnowThyNumber example from gyan/examples to GOPATH/src/github.com/<user-id>/KnowThyNumber.
-  -  Now run `go run server.go`. This will start local server on 9000 port.
+  -  Copy the KnowThyNumber example from `gyan/examples` to `GOPATH/src/github.com/<user-id>/KnowThyNumber`
+  -  Now run `go run server.go`. This will start local server on `9000` port.
   -  We got deployed_url in the previous section. Get the openstack token using `openstack token issue`. 
-  -  Open the browser and navigate to http://localhost:9000. Provide the `deployed_url` and `token` in the app. Now you can draw any number in the canvas and use your model to predict it.
+  -  Open the browser and navigate to `http://localhost:9000`. Provide the `deployed_url` and `token` in the app. Now you can draw any number in the canvas and use your model to predict it.