diff --git a/docs/docs/versions/migrating_chains/constitutional_chain.ipynb b/docs/docs/versions/migrating_chains/constitutional_chain.ipynb
index c3729b67c5a..771145547cd 100644
--- a/docs/docs/versions/migrating_chains/constitutional_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/constitutional_chain.ipynb
@@ -17,6 +17,8 @@
    "source": [
     "[ConstitutionalChain](https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html) allowed for a LLM to critique and revise generations based on [principles](https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.models.ConstitutionalPrinciple.html), structured as combinations of critique and revision requests. For example, a principle might include a request to identify harmful content, and a request to rewrite the content.\n",
     "\n",
+    "`Constitutional AI principles` are based on the [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/pdf/2212.08073) paper.\n",
+    "\n",
     "In `ConstitutionalChain`, this structure of critique requests and associated revisions was formatted into a LLM prompt and parsed out of string responses. This is more naturally achieved via [structured output](/docs/how_to/structured_output/) features of chat models. We can construct a simple chain in [LangGraph](https://langchain-ai.github.io/langgraph/) for this purpose. Some advantages of this approach include:\n",
     "\n",
     "- Leverage tool-calling capabilities of chat models that have been fine-tuned for this purpose;\n",
@@ -324,7 +326,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,