diff --git a/docs/modules/chains/examples/sqlite.ipynb b/docs/modules/chains/examples/sqlite.ipynb
index 548ca4c69..aaea8fbf8 100644
--- a/docs/modules/chains/examples/sqlite.ipynb
+++ b/docs/modules/chains/examples/sqlite.ipynb
@@ -56,6 +56,15 @@
     "llm = OpenAI(temperature=0)"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "3d1e692e",
+   "metadata": {},
+   "source": [
+    "**NOTE:** For data-sensitive projects, you can specify `return_direct=True` in the `SQLDatabaseChain` initialization to directly return the output of the SQL query without any additional formatting. This prevents the LLM from seeing any contents within the database. Note, however, the LLM still has access to the database scheme (i.e. dialect, table and key names) by default."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
diff --git a/langchain/chains/sql_database/prompt.py b/langchain/chains/sql_database/prompt.py
index 253b619ce..2b2a97cf6 100644
--- a/langchain/chains/sql_database/prompt.py
+++ b/langchain/chains/sql_database/prompt.py
@@ -26,7 +26,7 @@ PROMPT = PromptTemplate(
     template=_DEFAULT_TEMPLATE,
 )
 
-_DECIDER_TEMPLATE = """Given the below input question and list of potential tables, output a comma separated list of the table names that may be neccessary to answer this question.
+_DECIDER_TEMPLATE = """Given the below input question and list of potential tables, output a comma separated list of the table names that may be necessary to answer this question.
 
 Question: {query}