diff --git a/docs/code_demos/recipes/database_to_api.py b/docs/code_demos/recipes/database_to_api.py
index 8534f32..3b796ff 100644
--- a/docs/code_demos/recipes/database_to_api.py
+++ b/docs/code_demos/recipes/database_to_api.py
@@ -5,37 +5,47 @@
 import datetime as dt
 
 import aiohttp
-from etlhelper import iter_chunks
+import etlhelper as etl
 
+# import DbParams
 from db import ORACLE_DB
 
 logger = logging.getLogger("copy_sensors_async")
 
+# SQL query to get data from Oracle
 SELECT_SENSORS = """
     SELECT CODE, DESCRIPTION
     FROM BGS.DIC_SEN_SENSOR
     WHERE date_updated BETWEEN :startdate AND :enddate
     ORDER BY date_updated
     """
+
+# URL of API we want to send data to
 BASE_URL = "http://localhost:9200/"
+# Headers to tell the API we are sending data in JSON format
 HEADERS = {"Content-Type": "application/json"}
 
 
 def copy_sensors(startdate: dt.datetime, enddate: dt.datetime) -> None:
-    """Read sensors from Oracle and post to REST API."""
+    """Read sensors from Oracle and post to REST API.
+       Requires startdate amd enddate to filter to rows changed in a certain time period.
+    """
     logger.info("Copying sensors with timestamps from %s to %s",
                 startdate.isoformat(), enddate.isoformat())
     row_count = 0
 
+    # Connect using the imported DbParams
     with ORACLE_DB.connect("ORACLE_PASSWORD") as conn:
         # chunks is a generator that yields lists of dictionaries
-        chunks = iter_chunks(
+        # passing in our select query, connection object, bind variable parameters and custom transform function
+        chunks = etl.iter_chunks(
             SELECT_SENSORS,
             conn,
             parameters={"startdate": startdate, "enddate": enddate},
             transform=transform_sensors,
         )
 
+        # for each chunk of rows, synchronously post them to API
         for chunk in chunks:
             result = asyncio.run(post_chunk(chunk))
             row_count += len(result)
@@ -65,10 +75,14 @@ def transform_sensors(chunk: list[tuple]) -> list[tuple]:
 
 async def post_chunk(chunk: list[tuple]) -> list:
     """Post multiple items to API asynchronously."""
+    # initialize aiohttp session
     async with aiohttp.ClientSession() as session:
         # Build list of tasks
         tasks = []
+        # add each row to list of tasks for aiohttp to execute
         for item in chunk:
+            # a task is the instance of a function being executed with distinct arguments
+            #   in this case, the post_one function with argument of a dictionary representing a row of data
             tasks.append(post_one(item, session))
 
         # Process tasks in parallel.  An exception in any will be raised.
@@ -83,6 +97,7 @@ async def post_one(item: tuple, session: aiohttp.ClientSession) -> int:
     response = await session.post(
         BASE_URL + "sensors/_doc",
         headers=HEADERS,
+        # convert python dict to json object
         data=json.dumps(item),
     )
 
@@ -108,6 +123,6 @@ async def post_one(item: tuple, session: aiohttp.ClientSession) -> int:
     logger.setLevel(logging.INFO)
     logger.addHandler(handler)
 
-    # Copy data from 1 January 2000 to 00:00:00 today
+    # Copy data that was updated between 1 January 2000 to 00:00:00 today
     today = dt.datetime.combine(dt.date.today(), dt.time.min)
     copy_sensors(dt.datetime(2000, 1, 1), today)
diff --git a/docs/etl_functions/extract.rst b/docs/etl_functions/extract.rst
index fe3eb88..f5e031c 100644
--- a/docs/etl_functions/extract.rst
+++ b/docs/etl_functions/extract.rst
@@ -56,6 +56,8 @@ Keyword arguments
 All extract functions are derived from :func:`iter_chunks() <etlhelper.iter_chunks>`
 and take the same keyword arguments, which are passed through.
 
+.. _parameters:
+
 parameters
 """"""""""
 
diff --git a/docs/recipes/database_to_api.rst b/docs/recipes/database_to_api.rst
index 903341b..47468e6 100644
--- a/docs/recipes/database_to_api.rst
+++ b/docs/recipes/database_to_api.rst
@@ -7,9 +7,13 @@ ETL for posting data from a database into an HTTP API. The API could be
 a NoSQL document store (e.g. ElasticSearch, Cassandra) or some other web
 service.
 
-This example transfers data from Oracle to ElasticSearch. It uses
-``iter_chunks`` to fetch data from the database without loading it all
-into memory at once. A custom transform function creates a dictionary
+This example posts data from an Oracle database to an HTTP API. It uses
+:func:`iter_chunks() <etlhelper.iter_chunks>` to fetch data from the
+database without loading it all
+into memory at once.  :ref:`Parameters <parameters>` are sent with the database query to filter
+rows to only those changed within specified time period. This is used to
+only transfer data that has changed since the last time this script was 
+ran. A custom transform function creates a dictionary
 structure from each row of data. This is “dumped” into JSON and posted
 to the API via ``aiohttp``.
 
@@ -23,3 +27,7 @@ transfer as opposed to posting records in series.
 
 In this example, failed rows will fail the whole job. Removing the
 ``raise_for_status()`` call will let them just be logged instead.
+
+To provide the database connection, :class:`DbParams <etlhelper.DbParams>` object is
+imported from a separate `db` file.
+