mark as deprecated in readme

README.md

@@ -1,16 +1,5 @@
-# Retrieval Augmented Generation
+# ⚠️ NOTICE
 
-Please visit http://ai-cookbook.io for the accompanying documentation for this repo.
+This repo and http://ai-cookbook.io have been deprecated. Please visit the [AWS](https://docs.databricks.com/aws/en/generative-ai/tutorials/ai-cookbook/) or [Azure](https://learn.microsoft.com/en-us/azure/databricks/generative-ai/tutorials/ai-cookbook/) Databricks documentation for the latest content.
 
-This repo provides [learning materials](https://ai-cookbook.io/) and [production-ready code](https://github.com/databricks/genai-cookbook/tree/v0.2.0/agent_app_sample_code) to build a **high-quality RAG application** using Databricks. The [Mosaic Generative AI Cookbook](https://ai-cookbook.io/) provides:
-  - A conceptual overview and deep dive into various Generative AI design patterns, such as Prompt Engineering, Agents, RAG, and Fine Tuning
-  - An overview of Evaluation-Driven development
-  - The theory of every parameter/knob that impacts quality
-  - How to root cause quality issues and detemermine which knobs are relevant to experiment with for your use case
-  - Best practices for how to experiment with each knob
-
-The [provided code](https://github.com/databricks/genai-cookbook/tree/v0.2.0/agent_app_sample_code) is intended for use with the Databricks platform.  Specifically:
-- [Mosaic AI Agent Framework](https://docs.databricks.com/en/generative-ai/retrieval-augmented-generation.html) which provides a fast developer workflow with enterprise-ready LLMops & governance
-- [Mosaic AI Agent Evaluation](https://docs.databricks.com/en/generative-ai/agent-evaluation/index.html) which provides reliable, quality measurement using proprietary AI-assisted LLM judges to measure quality metrics that are powered by human feedback collected through an intuitive web-based chat UI
-
-![Alt text](rag_app_sample_code/dbxquality.png)
+To see the old README.md, go [here](./old_README.md).

old_README.md

@@ -0,0 +1,18 @@
+# Retrieval Augmented Generation
+
+Please visit http://ai-cookbook.io for the accompanying documentation for this repo.
+
+This repo provides [learning materials](https://ai-cookbook.io/) and [production-ready code](https://github.com/databricks/genai-cookbook/tree/v0.2.0/agent_app_sample_code) to build a **high-quality RAG application** using Databricks. The [Mosaic Generative AI Cookbook](https://ai-cookbook.io/) provides:
+
+- A conceptual overview and deep dive into various Generative AI design patterns, such as Prompt Engineering, Agents, RAG, and Fine Tuning
+- An overview of Evaluation-Driven development
+- The theory of every parameter/knob that impacts quality
+- How to root cause quality issues and detemermine which knobs are relevant to experiment with for your use case
+- Best practices for how to experiment with each knob
+
+The [provided code](https://github.com/databricks/genai-cookbook/tree/v0.2.0/agent_app_sample_code) is intended for use with the Databricks platform. Specifically:
+
+- [Mosaic AI Agent Framework](https://docs.databricks.com/en/generative-ai/retrieval-augmented-generation.html) which provides a fast developer workflow with enterprise-ready LLMops & governance
+- [Mosaic AI Agent Evaluation](https://docs.databricks.com/en/generative-ai/agent-evaluation/index.html) which provides reliable, quality measurement using proprietary AI-assisted LLM judges to measure quality metrics that are powered by human feedback collected through an intuitive web-based chat UI
+
+![Alt text](rag_app_sample_code/dbxquality.png)