diff --git a/docs/blog/rag-is-not-enough.md b/docs/blog/rag-is-not-enough.md
index 530b19e24..d2c5f87a6 100644
--- a/docs/blog/rag-is-not-enough.md
+++ b/docs/blog/rag-is-not-enough.md
@@ -27,7 +27,7 @@ We present a straightforward approach to adapting small, open-source models for
 
 In short, (1) extending a general foundation model like [Mistral](https://huggingface.co/mistralai/Mistral-7B-v0.1) with strong math and coding, and (2) training it over a high-quality, synthetic dataset generated from the intended corpus, and (3) adding RAG capabilities, can lead to significant accuracy improvements.
 
-Problems still arise with catastrophic forgetting in general tasks, commonly observed during continued fine-tuning [1]. In our case, this is likely exacerbated by our lack of access to Mistral’s original training dataset and various compression techniques used in our approach to keep the model small.
+Problems still arise with catastrophic forgetting in general tasks, commonly observed during specilizied domain fine-tuning. In our case, this is likely exacerbated by our lack of access to Mistral’s original training dataset and various compression techniques used in our approach to keep the model small.
 
 ## Selecting a strong foundation model