Update layout/about.md

layout/about.md

@@ -5,11 +5,13 @@
 
 ## What?
 
-This Gradio app is a demo showcasing a meeting Q&A application that retrieves multiple vtt transcripts, uploads them into pinecone as storage, and answers questions using the [Llama3.1 model](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). Unfortunately, The lack of a persistent GPU on Hugginface Zero spaces posed some challenges in using a [fine tuned model](https://huggingface.co/tykiww/llama3-8b-meetingQA) based on instruction tuned alpaca datasets and a noisy synthetic dataset of over 3000+ product, technical, and academic meetings. However, the outputs should still prove a massive improvement over the base Llama3 family of models.
+This app is a demo showcasing a meeting Q&A application that retrieves multiple vtt transcripts, uploads them into pinecone as storage, and answers questions using the [Llama3.1 model](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). 
+
+Unfortunately, The lack of a persistent GPU on Hugginface Zero spaces posed some challenges in using a [fine tuned model](https://huggingface.co/tykiww/llama3-8b-meetingQA) based on instruction tuned alpaca datasets and a noisy synthetic dataset of over 3000+ product, technical, and academic meetings. However, the outputs should still prove a massive improvement over the base Llama3 family of models.
 
 ## Why?
 
-The goal of the demo is to show you how RAG, prompt-engineering, and fine-tuning can all come together to enhance specific use-cases like meeting querying. This Q&A service seeks to look beyond "summarization" and "next steps" to create a customizable parser that can extract user-defined questions for enhanced specificity.
+**The value** of a tool like this is the ability to retrieve only the most necessary context and analysis from *multiple* documents. This means that you can easily scale the information and question retrieval around almost any problem structure (think stringing together 50+ documents and then chaining the LLMs to attack multiple structured questions in providing a tailored report).
 
 ## How?
 
@@ -19,7 +21,7 @@ Just start by following the guide below:
 2) ⤮ Wait for your file to be stored in the vector database.
 3) ❓ Query the meeting!
 
-Or, just skip right to step 3 since there are already some meetings in the database to query from!
+Or, just skip ⏭️ right to step 3 since there are already some meetings in the database to query from!
 
 
 This demo is just a peek and is subject to a demand queue. More to come!