diff --git a/examples/evaluation/Evaluate_RAG_with_LlamaIndex.ipynb b/examples/evaluation/Evaluate_RAG_with_LlamaIndex.ipynb
index d7090819..f54e4872 100644
--- a/examples/evaluation/Evaluate_RAG_with_LlamaIndex.ipynb
+++ b/examples/evaluation/Evaluate_RAG_with_LlamaIndex.ipynb
@@ -33,7 +33,7 @@
         "\n",
         "LLMs are trained on vast datasets, but these will not include your specific data. Retrieval-Augmented Generation (RAG) addresses this by dynamically incorporating your data during the generation process. This is done not by altering the training data of LLMs, but by allowing the model to access and utilize your data in real-time to provide more tailored and contextually relevant responses.\n",
         "\n",
-        "In RAG, your data is loaded and and prepared for queries or “indexed”. User queries act on the index, which filters your data down to the most relevant context. This context and your query then go to the LLM along with a prompt, and the LLM provides a response.\n",
+        "In RAG, your data is loaded and prepared for queries or “indexed”. User queries act on the index, which filters your data down to the most relevant context. This context and your query then go to the LLM along with a prompt, and the LLM provides a response.\n",
         "\n",
         "Even if what you’re building is a chatbot or an agent, you’ll want to know RAG techniques for getting data into your application."
       ]