diff --git a/docs/latest/guides/evaluation.mdx b/docs/latest/guides/evaluation.mdx
index 9e45c3cbd..ea93dbf8e 100644
--- a/docs/latest/guides/evaluation.mdx
+++ b/docs/latest/guides/evaluation.mdx
@@ -14,18 +14,6 @@ To get started using Haystack for evaluation, we recommend having a look at our
-## Datasets
-
-Annotated datasets are crucial for evaluating the retrieval as well as the question answering capabilities of your system.
-Haystack is designed to work with question answering datasets that follow SQuAD format.
-Please check out our [annotation tool](/guides/annotation) if you're interested in creating your own dataset.
-
-
-
-**Data Tool:** have a look at our `SquadData` object in `haystack/squad_data.py` if you'd like to manipulate SQuAD style data using Pandas dataframes.
-
-
-
## Open vs Closed Domain
There are two evaluation modes known as **open domain** and **closed domain.**
@@ -89,4 +77,16 @@ While F1 and EM would both score “one hundred percent” as sharing zero simil
SAS is particularly useful to seek out cases where F1 doesn't give a good indication of the validity of a predicted answer.
-You can read more about SAS in [this paper](https://arxiv.org/abs/2108.06130).
\ No newline at end of file
+You can read more about SAS in [this paper](https://arxiv.org/abs/2108.06130).
+
+## Datasets
+
+Annotated datasets are crucial for evaluating the retrieval as well as the question answering capabilities of your system.
+Haystack is designed to work with question answering datasets that follow SQuAD format.
+Please check out our [annotation tool](/guides/annotation) if you're interested in creating your own dataset.
+
+
+
+**Data Tool:** have a look at our `SquadData` object in `haystack/squad_data.py` if you'd like to manipulate SQuAD style data using Pandas dataframes.
+
+
\ No newline at end of file