chore: added note about consultation (#98)

jjmachan · web-flow · commit a55c3be8b238 · 2023-08-24T20:23:52.000+05:30
diff --git a/README.md b/README.md
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     <p>
 </h4>
 
+> Note: We're working with a few select organisations, leveraging what we learned building Ragas, to improve the reliability of their RAG systems in production. Due to our small team size we can only work with a few clients so do fill out [this form](https://forms.gle/tk9VZMaeybxQATU69) or write to us at [team@explodinggradients.com](mailto:team@explodinggradients.com) and we will get back 🙂
+
 ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard... This is where ragas (RAG Assessment) comes in
 
 ragas provides you with the tools based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. ragas can be integrated with your CI/CD to provide continuous checks to ensure performance.

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     <p>
 </h4>
 +> Note: We're working with a few select organisations, leveraging what we learned building Ragas, to improve the reliability of their RAG systems in production. Due to our small team size we can only work with a few clients so do fill out [this form](https://forms.gle/tk9VZMaeybxQATU69) or write to us at [[email protected]](mailto:[email protected]) and we will get back 🙂
++
 ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard... This is where ragas (RAG Assessment) comes in
 ragas provides you with the tools based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. ragas can be integrated with your CI/CD to provide continuous checks to ensure performance.