diff --git a/docs/source/demo.md b/docs/source/demo.md
index 0445e0c5..60b981ea 100644
--- a/docs/source/demo.md
+++ b/docs/source/demo.md
@@ -50,7 +50,7 @@ DONE
 
 ## Predicting a list of audio files
 
-To predict a list of audio files, you can use `--list' argument. An example is given below. Notice, that you need to specify the relative path of audio file in that list if it is not relative to the current directory. Additonally, you can add`--outfile` argument to save the result to a file.
+To predict a list of audio files, you can use `--list` argument. An example is given below. Notice, that you need to specify the relative path of audio file in that list if it is not relative to the current directory. Additonally, you can add`--outfile` argument to save the result to a file.
 
 ```bash
 (.env) python3 -m nkululeko.demo --config data/ravdess/exp1.ini --list data/ravdess/ravdess_test.csv --folder data/ravdess/ --outfile /tmp/ravdess_test_predict.csv
diff --git a/docs/source/hello_world_csv.md b/docs/source/hello_world_csv.md
index 15d2bccf..9ceda1dd 100644
--- a/docs/source/hello_world_csv.md
+++ b/docs/source/hello_world_csv.md
@@ -1,4 +1,4 @@
-# Hello World with [CSV]
+# Hello World [CSV]
 
 In the previous tutorial, we learned how to use Nkululeko with audformat dataset.
 Since most dataset are not in audformat, we will learn how to use Nkululeko with pre-processed CSV dataset.
diff --git a/docs/source/how_to.md b/docs/source/how_to.md
index 9617c3c3..29683d59 100644
--- a/docs/source/how_to.md
+++ b/docs/source/how_to.md
@@ -1,4 +1,4 @@
-# How to set up your first nkululeko project
+# Nkululeko Workflow
 
 Nkululeko is a framework to build machine learning models that recognize
 speaker characteristics on a very high level of abstraction (i.e.
@@ -7,7 +7,7 @@ starting without programming experience).
 This post is meant to help you with setting up your first experiment,
 based on the Berlin Emodb.
 
-1) Set up python
+1) Set up Python
 
 It's written in python, so first you have to set up a Python environment.
 It is recommended to use Linux-based systems for easiness, but it should work on Windows as well.
@@ -16,10 +16,10 @@ The current version of nkululeko is tested with Python 3.8.5.
 2) Get a database
 
 Load the Berlin emodb database to some location on you harddrive, as
-discussed in [this post](http://blog.syntheticspeech.de/2021/08/10/get-all-information-from-emodb/). I will refer to the location as "emodb root"
-from now on.
+discussed in [this post](http://blog.syntheticspeech.de/2021/08/10/get-all-information-from-emodb/). 
+I will refer to the location as "emodb root" from now on. You can also follow [Hello World page](hello_world_aud.md).
 
-3) Install nkululeko
+3) Install Nkululeko
 
 Inside your virtual environment, run:
 
@@ -28,7 +28,7 @@ Inside your virtual environment, run:
 This should install nkululeko and all required modules. It takes a long
 time and a lot of space, when done intially.
 
-5) Adapt the ini file
+5) Adapt the INI file
 
 Use your favourite editor, e.g., Visual Studio code and edit the file
 that defines your experiment. You might start with this demo sample. You
@@ -40,7 +40,7 @@ this:
 
     emodb = /home/felix/data/audb/emodb
 
-An overview on all nkululeko options should be here.
+An overview on all nkululeko options is listed in [INI file](ini_file).
 
 6) Run the experiment