From 17ed9c440be7c7d225b55685a8f1b6224f6d7a9f Mon Sep 17 00:00:00 2001 From: Expeto Date: Tue, 11 Feb 2025 15:11:19 +0300 Subject: [PATCH] Update content/guides/genai-leveraging-rag/index.md Co-authored-by: Craig Osterhout <103533812+craig-osterhout@users.noreply.github.com> --- content/guides/genai-leveraging-rag/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/guides/genai-leveraging-rag/index.md b/content/guides/genai-leveraging-rag/index.md index b99abe1d8e0..f7c8815d46c 100644 --- a/content/guides/genai-leveraging-rag/index.md +++ b/content/guides/genai-leveraging-rag/index.md @@ -54,7 +54,7 @@ Another key difference lies in schema flexibility. SQL databases operate on a ri To illustrate the power of RAG systems in practice, let's examine a real-world implementation using Apache NiFi as our subject matter. This case study demonstrates how RAG can enhance an AI's ability to provide accurate, contextual information about specialized technical topics. -### Teaching AI About New Technologies +### Teaching AI about new technologies Apache NiFi serves as an excellent example of the limitations of traditional LLMs and how RAG can overcome them. As a relatively recent technology, many LLMs have limited or outdated knowledge about it, making it a perfect candidate for demonstrating RAG's capabilities.