diff --git a/awswrangler/athena/_read.py b/awswrangler/athena/_read.py
index 7edd84ffe..5501f89e8 100644
--- a/awswrangler/athena/_read.py
+++ b/awswrangler/athena/_read.py
@@ -807,9 +807,11 @@ def read_sql_query(  # pylint: disable=too-many-arguments,too-many-locals
 
     There are two batching strategies:
 
-    - If **chunksize=True**, a new DataFrame will be returned for each file in the query result.
+    - If **chunksize=True**, depending on the size of the data, one or more data frames will be
+      returned per each file in the query result.
+      Unlike **chunksize=INTEGER**, rows from different files will not be mixed in the resulting data frames.
 
-    - If **chunksize=INTEGER**, awswrangler will iterate on the data by number of rows igual the received INTEGER.
+    - If **chunksize=INTEGER**, awswrangler will iterate on the data by number of rows egual the received INTEGER.
 
     `P.S.` `chunksize=True` is faster and uses less memory while `chunksize=INTEGER` is more precise
     in number of rows for each Dataframe.
@@ -1110,9 +1112,11 @@ def read_sql_table(
 
     There are two batching strategies:
 
-    - If **chunksize=True**, a new DataFrame will be returned for each file in the query result.
+    - If **chunksize=True**, depending on the size of the data, one or more data frames will be
+      returned per each file in the query result.
+      Unlike **chunksize=INTEGER**, rows from different files will not be mixed in the resulting data frames.
 
-    - If **chunksize=INTEGER**, awswrangler will iterate on the data by number of rows igual the received INTEGER.
+    - If **chunksize=INTEGER**, awswrangler will iterate on the data by number of rows egual the received INTEGER.
 
     `P.S.` `chunksize=True` is faster and uses less memory while `chunksize=INTEGER` is more precise
     in number of rows for each Dataframe.
diff --git a/awswrangler/redshift.py b/awswrangler/redshift.py
index d82444fdc..b28c68884 100644
--- a/awswrangler/redshift.py
+++ b/awswrangler/redshift.py
@@ -1122,7 +1122,9 @@ def unload(
 
     There are two batching strategies on awswrangler:
 
-    - If **chunked=True**, a new DataFrame will be returned for each file in your path/dataset.
+    - If **chunked=True**, depending on the size of the data, one or more data frames will be
+      returned per each file in the path/dataset.
+      Unlike **chunked=INTEGER**, rows from different files will not be mixed in the resulting data frames.
 
     - If **chunked=INTEGER**, awswrangler will iterate on the data by number of rows (equal to the received INTEGER).
 
diff --git a/awswrangler/s3/_read_parquet.py b/awswrangler/s3/_read_parquet.py
index 40a2263ce..e32c26d49 100644
--- a/awswrangler/s3/_read_parquet.py
+++ b/awswrangler/s3/_read_parquet.py
@@ -600,9 +600,11 @@ def read_parquet(
 
     There are two batching strategies on awswrangler:
 
-    - If **chunked=True**, a new DataFrame will be returned for each file in your path/dataset.
+    - If **chunked=True**, depending on the size of the data, one or more data frames will be
+      returned per each file in the path/dataset.
+      Unlike **chunked=INTEGER**, rows from different files will not be mixed in the resulting data frames.
 
-    - If **chunked=INTEGER**, awswrangler will iterate on the data by number of rows igual the received INTEGER.
+    - If **chunked=INTEGER**, awswrangler will iterate on the data by number of rows egual the received INTEGER.
 
     `P.S.` `chunked=True` if faster and uses less memory while `chunked=INTEGER` is more precise
     in number of rows for each Dataframe.
@@ -652,7 +654,7 @@ def read_parquet(
     chunked : Union[int, bool]
         If passed will split the data in a Iterable of DataFrames (Memory friendly).
         If `True` awswrangler iterates on the data by files in the most efficient way without guarantee of chunksize.
-        If an `INTEGER` is passed awswrangler will iterate on the data by number of rows igual the received INTEGER.
+        If an `INTEGER` is passed awswrangler will iterate on the data by number of rows egual the received INTEGER.
     dataset: bool
         If `True` read a parquet dataset instead of simple file(s) loading all the related partitions as columns.
     categories: Optional[List[str]], optional
@@ -830,10 +832,12 @@ def read_parquet_table(
 
     There are two batching strategies on awswrangler:
 
-    - If **chunked=True**, a new DataFrame will be returned for each file in your path/dataset.
+    - If **chunked=True**, depending on the size of the data, one or more data frames will be
+      returned per each file in the path/dataset.
+      Unlike **chunked=INTEGER**, rows from different files will not be mixed in the resulting data frames.
 
     - If **chunked=INTEGER**, awswrangler will paginate through files slicing and concatenating
-      to return DataFrames with the number of row igual the received INTEGER.
+      to return DataFrames with the number of rows egual the received INTEGER.
 
     `P.S.` `chunked=True` if faster and uses less memory while `chunked=INTEGER` is more precise
     in number of rows for each Dataframe.