From a81006031efc76a9f7b656e0ddabd4ea8473b6d9 Mon Sep 17 00:00:00 2001
From: Carlotta Castelluccio <82521518+carlotta94c@users.noreply.github.com>
Date: Thu, 2 Nov 2023 11:53:13 +0100
Subject: [PATCH] Removing reference to startup name

---
 08-building-search-applications/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/08-building-search-applications/README.md b/08-building-search-applications/README.md
index 988251ff2..439b5e39a 100644
--- a/08-building-search-applications/README.md
+++ b/08-building-search-applications/README.md
@@ -6,7 +6,7 @@
 
 There's more to LLMs than chat bots and text generation. It's also possible to build search applications using Embeddings. Embeddings are numerical representations of data also known as vectors, and can be used for semantic search for data.
 
-In this lesson, you are going to build a search application for our education startup Edu4All. Our startup is a non-profit organization that provides free education to students in developing countries. Our startup has a large number of YouTube videos that students can use to learn about AI. Our startup wants to build a search application that allows students to search for a YouTube video by typing a question.
+In this lesson, you are going to build a search application for our education startup. Our startup is a non-profit organization that provides free education to students in developing countries. Our startup has a large number of YouTube videos that students can use to learn about AI. Our startup wants to build a search application that allows students to search for a YouTube video by typing a question.
 
 For example, a student might type in 'What are Jupyter Notebooks?' or 'What is Azure ML' and the search application will return a list of YouTube videos that are relevant to the question, and better still, the search application will return a link to the place in the video where the answer to the question is located.