Editorial

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/rag/_demo.md

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 title: Run a llama.cpp chatbot powered by Arm Kleidi technology
+weight: 2
 
 overview: | 
   This Learning Path shows you how to use a c4a-highcpu-72 Google Axion instance powered by an Arm Neoverse CPU to build a simple Token-as-a-Service (TaaS) RAG-enabled server that you can then use to provide a chatbot to serve a small number of concurrent users.

content/learning-paths/servers-and-cloud-computing/rag/backend.md

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 title: Deploy a RAG-based LLM backend server
-weight: 3
+weight: 4
 
 layout: learningpathall
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content/learning-paths/servers-and-cloud-computing/rag/chatbot.md

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 title: The RAG Chatbot and its Performance
-weight: 5
+weight: 6
 
 layout: learningpathall
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content/learning-paths/servers-and-cloud-computing/rag/frontend.md

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 title: Deploy RAG-based LLM frontend server
-weight: 4
+weight: 5
 
 layout: learningpathall
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content/learning-paths/servers-and-cloud-computing/rag/rag_llm.md

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 # User change
 title: "Set up a RAG based LLM Chatbot"
 
-weight: 2 # 1 is first, 2 is second, etc.
+weight: 3 
 
 # Do not modify these elements
 layout: "learningpathall"