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* updated docs (again)

* updated the LLMs section, prompt processing section and the RAG section of the docs

---------

Co-authored-by: Salman Paracha <salmanparacha@MacBook-Pro-261.local>
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10
docs/source/getting_started/use_cases/rag.rst
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@ -1,10 +1,12 @@
.. _arch_rag_guide:
Retrieval-Augmented (RAG)
====================================
=========================
The following section describes how Arch can help you build faster, smarter and more accurate
Retrieval-Augmented Generation (RAG) applications.
Intent-drift detection
Intent-drift Detection
----------------------
Developers struggle to handle `follow-up <https://www.reddit.com/r/ChatGPTPromptGenius/comments/17dzmpy/how_to_use_rag_with_conversation_history_for/?>`_
@ -65,8 +67,8 @@ You can used the last set of messages that match to an intent to prompt an LLM,
improved retrieval, etc. With Arch and a few lines of code, you can improve the retrieval accuracy, lower overall
token cost and dramatically improve the speed of their responses back to users.
Smarter retrival with parameter extraction
------------------------------------------
Parameter Extraction for RAG
----------------------------
To build RAG (Retrieval-Augmented Generation) applications, you can configure prompt targets with parameters,
enabling Arch to retrieve critical information in a structured way for processing. This approach improves the