diff --git a/doc/source/tutorial-quickstart-pytorch-lightning.rst b/doc/source/tutorial-quickstart-pytorch-lightning.rst
index acfbecf41260..7c74c9a1682f 100644
--- a/doc/source/tutorial-quickstart-pytorch-lightning.rst
+++ b/doc/source/tutorial-quickstart-pytorch-lightning.rst
@@ -1,12 +1,119 @@
 .. _quickstart-pytorch-lightning:
 
+##############################
+ Quickstart PyTorch Lightning
+##############################
 
-Quickstart PyTorch Lightning
-============================
+In this federated learning tutorial we will learn how to train an
+AutoEncoder model on MNIST using Flower and PyTorch Lightning. It is
+recommended to create a virtual environment and run everything within a
+:doc:`virtualenv <contributor-how-to-set-up-a-virtual-env>`.
 
-.. meta::
-   :description: Check out this Federated Learning quickstart tutorial for using Flower with PyTorch Lightning to train an Auto Encoder model on MNIST.
+Then, clone the code example directly from GitHub:
 
-Let's build a horizontal federated learning system using PyTorch Lightning and Flower!
+.. code:: shell
 
-Please refer to the `full code example <https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-lightning>`_ to learn more.
+   git clone --depth=1 https://github.com/adap/flower.git _tmp \
+                && mv _tmp/examples/quickstart-pytorch-lightning . \
+                && rm -rf _tmp && cd quickstart-pytorch-lightning
+
+This will create a new directory called `quickstart-pytorch-lightning`
+containing the following files:
+
+.. code:: shell
+
+   quickstart-pytorch-lightning
+   ├── pytorchlightning_example
+   │   ├── client_app.py   # Defines your ClientApp
+   │   ├── server_app.py   # Defines your ServerApp
+   │   └── task.py         # Defines your model, training and data loading
+   ├── pyproject.toml      # Project metadata like dependencies and configs
+   └── README.md
+
+Next, activate your environment, then run:
+
+.. code:: shell
+
+   # Navigate to the example directory
+   $ cd path/to/quickstart-pytorch-lightning
+
+   # Install project and dependencies
+   $ pip install -e .
+
+By default, Flower Simulation Engine will be started and it will create
+a federation of 4 nodes using `FedAvg
+<https://flower.ai/docs/framework/ref-api/flwr.server.strategy.FedAvg.html#flwr.server.strategy.FedAvg>`_
+as the aggregation strategy. The dataset will be partitioned using
+Flower Dataset's `IidPartitioner
+<https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner>`_.
+To run the project, do:
+
+.. code:: shell
+
+   # Run with default arguments
+   $ flwr run .
+
+With default arguments you will see an output like this one:
+
+.. code:: shell
+
+   Loading project configuration...
+   Success
+   INFO :      Starting Flower ServerApp, config: num_rounds=3, no round_timeout
+   INFO :
+   INFO :      [INIT]
+   INFO :      Using initial global parameters provided by strategy
+   INFO :      Starting evaluation of initial global parameters
+   INFO :      Evaluation returned no results (`None`)
+   INFO :
+   INFO :      [ROUND 1]
+   INFO :      configure_fit: strategy sampled 2 clients (out of 4)
+   INFO :      aggregate_evaluate: received 2 results and 0 failures
+   WARNING :   No evaluate_metrics_aggregation_fn provided
+   INFO :
+   INFO :      [ROUND 2]
+   INFO :      configure_fit: strategy sampled 2 clients (out of 4)
+   INFO :      aggregate_fit: received 2 results and 0 failures
+   INFO :      configure_evaluate: strategy sampled 2 clients (out of 4)
+   INFO :      aggregate_evaluate: received 2 results and 0 failures
+   INFO :
+   INFO :      [ROUND 3]
+   INFO :      configure_fit: strategy sampled 2 clients (out of 4)
+   INFO :      aggregate_fit: received 2 results and 0 failures
+   INFO :      configure_evaluate: strategy sampled 2 clients (out of 4)
+   INFO :      aggregate_evaluate: received 2 results and 0 failures
+   INFO :
+   INFO :      [SUMMARY]
+   INFO :      Run finished 3 round(s) in 136.92s
+   INFO :          History (loss, distributed):
+   INFO :                  round 1: 0.04982871934771538
+   INFO :                  round 2: 0.046457378193736076
+   INFO :                  round 3: 0.04506748169660568
+   INFO :
+
+Each simulated `ClientApp` (two per round) will also log a summary of
+their local training process. Expect this output to be similar to:
+
+.. code:: shell
+
+   # The left part indicates the process ID running the `ClientApp`
+   (ClientAppActor pid=38155) ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+   (ClientAppActor pid=38155) ┃        Test metric        ┃       DataLoader 0        ┃
+   (ClientAppActor pid=38155) ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+   (ClientAppActor pid=38155) │         test_loss         │   0.045175597071647644    │
+   (ClientAppActor pid=38155) └───────────────────────────┴───────────────────────────┘
+
+You can also override the parameters defined in the
+``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this:
+
+.. code:: shell
+
+   # Override some arguments
+   $ flwr run . --run-config num-server-rounds=5
+
+.. note::
+
+   Check the `source code
+   <https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-lightning>`_
+   of this tutorial in ``examples/quickstart-pytorch-lightning`` in the
+   Flower GitHub repository.