From 31f14469008599b6bb0be4e74aeb409f2ccc7581 Mon Sep 17 00:00:00 2001 From: Ikko Eltociear Ashimine Date: Sat, 21 Sep 2024 00:05:37 +0900 Subject: [PATCH] docs: update Evaluate_RAG_with_LlamaIndex.ipynb minor fix --- examples/evaluation/Evaluate_RAG_with_LlamaIndex.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/evaluation/Evaluate_RAG_with_LlamaIndex.ipynb b/examples/evaluation/Evaluate_RAG_with_LlamaIndex.ipynb index d709081985..f54e4872a1 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." ]