diff --git a/lessons/05_joins.md b/lessons/05_joins.md
index 2421a32..973b61b 100644
--- a/lessons/05_joins.md
+++ b/lessons/05_joins.md
@@ -188,6 +188,14 @@ result.select("PatID", "RxDate", "Rx").unique()
 df.height  # same as len(df), but clearer intent
 ```
 
+**`pl.read_parquet(path)`:** Load a Parquet file into a DataFrame. You'll use this at the start of most exercises to load the tables you'll join.
+
+```python
+df_demographic = pl.read_parquet("data/demographic.parquet")
+df_enrollment = pl.read_parquet("data/enrollment.parquet")
+df_dispensing = pl.read_parquet("data/dispensing.parquet")
+```
+
 > **SAS reflex check:** IN= flags become how=. MERGE + IF a AND b ↔ how="inner"; IF a ↔ how="left"; IF a AND NOT b ↔ how="anti". But MERGE BY does a one-to-one walk within BY groups — join() always produces the full match combination, so expect row multiplication where SAS would have quietly aligned rows.
 
 ## Worked example
diff --git a/solutions/ex06_write_and_partition.py b/solutions/ex06_write_and_partition.py
index 1ea0cca..086b0b7 100644
--- a/solutions/ex06_write_and_partition.py
+++ b/solutions/ex06_write_and_partition.py
@@ -30,8 +30,8 @@ def main() -> None:
     # TODO 1: Read data/dispensing.parquet into a DataFrame called df.
     df = pl.read_parquet(DISPENSING_PATH)
 
-    # TODO 2: Write the 2021 dispensings to
-    # data/output/dispensing_2021.parquet (create OUTPUT_DIR first).
+    # TODO 2: Write the 2021 dispensings to data/output/dispensing_2021.parquet.
+    # Create OUTPUT_DIR first if it doesn't exist.
     OUTPUT_DIR.mkdir(exist_ok=True)
     df_2021 = df.filter(pl.col("RxDate").dt.year() == 2021)
     df_2021.write_parquet(str(OUTPUT_DIR / "dispensing_2021.parquet"))