diff --git a/notebooks/instructlab-knowledge/instructlab-knowledge.ipynb b/notebooks/instructlab-knowledge/instructlab-knowledge.ipynb
index 92369d6..69bdb54 100644
--- a/notebooks/instructlab-knowledge/instructlab-knowledge.ipynb
+++ b/notebooks/instructlab-knowledge/instructlab-knowledge.ipynb
@@ -600,6 +600,37 @@
     "NUM_SEED_EXAMPLES = 7"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "32e13a94-1c5e-4310-9500-6940368ec2ea",
+   "metadata": {},
+   "source": [
+    "#### [OPTIONAL] Prompt customization for Q&A Generation\n",
+    "\n",
+    "Optionally insert your own stylistic customization statement below. If `customization_str` is `None`, there will be no customization attempted and the default QA generation prompt will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78b51f57-7b7b-4d53-a129-29c291939dae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "customization_str = None \n",
+    "\n",
+    "# Example: \n",
+    "# customization_str = \"Write at the fifth grade level.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9d5191d-cbd7-4c4e-b5e1-748ed8684eaf",
+   "metadata": {},
+   "source": [
+    "### Run QA Generation"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -621,7 +652,8 @@
     "                           NUM_SEED_EXAMPLES,\n",
     "                           API_KEY,\n",
     "                           ENDPOINT_URL,\n",
-    "                           MODEL_NAME)\n",
+    "                           MODEL_NAME,\n",
+    "                           customization_str)\n",
     "    print(f\"qna.yaml saved to: {qna_output_path}\")\n"
    ]
   },
diff --git a/notebooks/instructlab-knowledge/utils/qna_gen.py b/notebooks/instructlab-knowledge/utils/qna_gen.py
index a277ae4..bd1a0d4 100644
--- a/notebooks/instructlab-knowledge/utils/qna_gen.py
+++ b/notebooks/instructlab-knowledge/utils/qna_gen.py
@@ -3,6 +3,8 @@
 import random
 
 from pathlib import Path
+
+from docling_sdg.qa.prompts.generation_prompts import QaPromptTemplate
 from pydantic import SecretStr
 from textwrap import wrap
 
@@ -11,6 +13,32 @@
 from docling_sdg.qa.generate import Generator
 from docling_sdg.qa.base import GenerateOptions, LlmProvider
 
+CUSTOM_COMBINED_QUESTION_PROMPT =  (
+    "I will provide you a text passage. I need you to generate three questions that "
+    "must be answered only with information contained in this passage, and nothing "
+    "else.\n"
+    'The first question is of type "fact_single", which means that the answer to this '
+    "question is a simple, single piece of factual information contained in the "
+    "context.\n"
+    'The second question is of type "summary", which means that the answer to this '
+    "question summarizes different pieces of factual information contained in the "
+    "context.\n"
+    'The third question is of type "reasoning", which is a question that requires the '
+    "reader to think critically and make an inference or draw a conclusion based on "
+    "the information provided in the passage.\n"
+    "Make sure that the three questions are different. Make sure that every question "
+    " has a provided answer\n\n"
+    "{customization_str}\n\n"
+    "You will format your generation as a python dictionary, such as:\n\n"
+    '{"fact_single": <The "fact_single" type question you thought of>, '
+    '"fact_single_answer: <Answer to the "fact_single" question>, "summary": <the '
+    '"summary" type question you thought of>, "summary_answer": <Answer to the '
+    '"summary" question>, "reasoning": <the "reasoning" type question you thought '
+    'of>, "reasoning_answer": <Answer to the "reasoning" question>}\n\n'
+    "Only provide the python dictionary as your output. Make sure you provide an answer for each question.\n\n"
+    "Context: {context_str}"
+)
+
 chunk_filter = [
     lambda chunk: len(str(chunk.text)) > 500
 ]
@@ -32,7 +60,7 @@ class IndentedDumper(yaml.Dumper):
     def increase_indent(self, flow=False, indentless=False):
         return super(IndentedDumper, self).increase_indent(flow, False)
 
-def generate_seed_examples(contribution_name: str, chunks_jsonl_path: Path, output_dir: Path, domain: str, summary: str, num_seed_examples: int, api_key: str, api_url: str, model_id: str) -> Path:
+def generate_seed_examples(contribution_name: str, chunks_jsonl_path: Path, output_dir: Path, domain: str, summary: str, num_seed_examples: int, api_key: str, api_url: str, model_id: str, customization_str: str | None = None) -> Path:
     """
     Creates a seed dataset from a path
     Args:
@@ -44,6 +72,7 @@ def generate_seed_examples(contribution_name: str, chunks_jsonl_path: Path, outp
         api_key (str):                  API key for the model used to generate questions and answers from contexts
         api_url (str):                  Endpoint for the model used to generate questions and answers from contexts
         model_id (str):                 Name of the model used to generate questions and answers from contexts
+        customization_str (str | None)  A directive for how to stylistically customize the generated QAs
     Returns:
         qna_output_path (pathlib.Path): Path to the generated seed example file
     """
@@ -89,6 +118,15 @@ def generate_seed_examples(contribution_name: str, chunks_jsonl_path: Path, outp
     generate_options.url = api_url
     generate_options.model_id = model_id
     generate_options.generated_file = output_dir / f"qagen-{contribution_name}.json"
+
+    if customization_str is not None:
+        generate_options.prompts = [QaPromptTemplate(
+            template=CUSTOM_COMBINED_QUESTION_PROMPT,
+            keys=["context_str", "customization_str"],
+            labels=["fact_single", "summary", "reasoning"],
+            type_="question",
+        )]
+
     gen = Generator(generate_options=generate_options)
 
     Path.unlink(generate_options.generated_file, missing_ok=True)