diff --git a/docs/notebooks/low_level.ipynb b/docs/notebooks/low_level.ipynb
index dfb56f9..7672662 100644
--- a/docs/notebooks/low_level.ipynb
+++ b/docs/notebooks/low_level.ipynb
@@ -8,7 +8,7 @@
     "# Lower-level interface for performance and flexibility\n",
     "## Reveal the hidden power of nested Series\n",
     "\n",
-    "This section is for users willing to work with nested data in pandas more computationally and memory efficiently, and having access to different data representations.\n",
+    "This section is for users looking to optimize the performance, both computationally and in memory-usage, of their workflows. This section also details a broader suite of data representations usable within `nested-pandas`.\n",
     "It shows how to deal with individual nested columns: add, remove, and modify data using both \"flat-array\" and \"list-array\" representations.\n",
     "It also demonstrates how to convert nested Series to and from different data types, like `pd.ArrowDtype`d Series, flat dataframes, list-array dataframes, and collections of nested elements."
    ]
@@ -71,7 +71,7 @@
     "\n",
     "`pandas` provides an interface to access series with custom \"accessors\" - special attributes acting like a different view on the data.\n",
     "You may already know [`.str` accessor](https://pandas.pydata.org/pandas-docs/stable/reference/series.html#api-series-str) for strings or [`.dt` for datetime-like](https://pandas.pydata.org/pandas-docs/stable/reference/series.html#timedelta-methods) data.\n",
-    "Since version 2 pandas also supports few accessors for `ArrowDtype`d Series, `.list` for list-arrays and `.struct` for struct-arrays.\n",
+    "Since v2.0, pandas also supports few accessors for `ArrowDtype`d Series, `.list` for list-arrays and `.struct` for struct-arrays.\n",
     "\n",
     "`nested-pandas` extends this concept and provides `.nest` accessor for `NestedDtype`d Series, which gives user an object to work with nested data more efficiently and flexibly."
    ]
@@ -83,7 +83,7 @@
    "source": [
     "### `.nest` object is a mapping\n",
     "\n",
-    "`.nest` accessor would give you an object implementing `Mapping` interface, so you can use it like a dictionary.\n",
+    "`.nest` accessor provides an object implementing `Mapping` interface, so you can use it like a dictionary.\n",
     "Keys of this mapping are the names of the nested columns (fields), and values are \"flat\" Series representing the nested data."
    ]
   },
@@ -130,7 +130,7 @@
    "id": "4b503d563196f8",
    "metadata": {},
    "source": [
-    "Value of each key is a \"flat\" Series with repeated index, so the original index of the `nested_series` is repeated for each element of the nested data. "
+    "The value of each key is a \"flat\" Series with repeated index, so the original index of the `nested_series` is repeated for each element of the nested data. "
    ]
   },
   {
@@ -221,7 +221,7 @@
     "- `.to_flat()` - get a \"flat\" pandas data frame with repeated index, it is kinda of a concatenation of all nested elements along the nested axis\n",
     "- `.to_lists()` - get a pandas data frame of nested-array (list-array) Series, where each element is a list of nested elements. Data type would be `pd.ArrowDtype` of pyarrow list.\n",
     "\n",
-    "Both representations are copy-free, so they could be done very efficiently. The only thing is happening when accessing any \"flat\" representation is a creation of a new repeating index."
+    "Both representations are copy-free, so they could be done very efficiently. The only additional overhead when accessing a \"flat\" representation is the creation of a new repeating index."
    ]
   },
   {