docs: Fix typos

INSTALL_AND_USAGE.md

@@ -4,8 +4,8 @@
 
 Before starting to install the LLM Twin project, make sure you have installed the following dependencies on your system:
 
-- (Docker ">=v27.0.3")[https://www.docker.com/]
-- (GNU Make ">=3.81")[https://www.gnu.org/software/make/]
+- [Docker ">=v27.0.3"](https://www.docker.com/)
+- [GNU Make ">=3.81"](https://www.gnu.org/software/make/)
 
 The whole LLM Twin application will be run locally using Docker. 
 
@@ -92,7 +92,7 @@ docker logs llm-twin-bytewax
 ```
 You should see logs reflecting the cleaning, chunking, and embedding operations (without any errors, of course).
 
-To check that the Qdrant `vector DB` is populated successfully, go to its dashboard at localhost:6333/dashboard. There, you should see the repositories or article collections created and populated.
+To check that the Qdrant `vector DB` is populated successfully, go to its dashboard at [localhost:6333/dashboard](localhost:6333/dashboard). There, you should see the repositories or article collections created and populated.
 
 > [!NOTE]
 > If using the cloud version of Qdrant, go to your Qdrant account and cluster to see the same thing as in the local dashboard.

README.md

@@ -1,6 +1,6 @@
 <div align="center">
     <h2>LLM Twin Course: Building Your Production-Ready AI Replica</h2>
-    <h1>Learn to build an end-to-end system for production-ready LLM & RAG systems by building your LLM Twin</h1>
+    <h1>Learn to architect and implement a production-ready LLM & RAG system by building your LLM Twin</h1>
     <h3>From data gathering to productionizing LLMs using LLMOps good practices.</h3>
     <i>by <a href="https://github.com/iusztinpaul">Paul Iusztin</a>, <a href="https://github.com/alexandruvesa">Alexandru Vesa</a> and <a href="https://github.com/Joywalker">Alexandru Razvant</a></i>
 </div>
@@ -153,7 +153,7 @@ To understand how to install and run the LLM Twin code, go to the [INSTALL_AND_U
 
 The bonus Superlinked series has an extra dedicated [README](https://github.com/decodingml/llm-twin-course/blob/main/6-bonus-superlinked-rag/README.md) that you can access under the [6-bonus-superlinked-rag](https://github.com/decodingml/llm-twin-course/tree/main/6-bonus-superlinked-rag) directory.
 
-Here we explain all the changes made to the code to run it with the improved RAG layer powered by [Superlinked](https://rebrand.ly/superlinked-github).
+In that section, we explain how to run it with the improved RAG layer powered by [Superlinked](https://rebrand.ly/superlinked-github).
 
 ## Meet your teachers!