docs: refactor core concepts

docs/concepts/components/index.md

@@ -0,0 +1,7 @@
+# Components Guide
+
+This guide provides an overview of the different components used inside Ragas.
+
+## [Prompt Object]()
+## [Evaluation Dataset]()
+## [Evaluation Sample]()

docs/concepts/index.md

@@ -1,42 +1,42 @@
 # 📚 Core Concepts
 
-Ragas aims to create an open standard, providing developers with the tools and techniques to leverage continual learning in their RAG applications. With Ragas, you would be able to
 
-1. Synthetically generate a diverse test dataset that you can use to evaluate your app.
-2. Use LLM-assisted evaluation metrics designed to help you objectively measure the performance of your application.
-3. Monitor the quality of your apps in production using smaller, cheaper models that can give actionable insights. For example, the number of hallucinations in the generated answer. 
-4. Use these insights to iterate and improve your application.
+<div class="grid cards" markdown>
 
+-   :material-widgets:{ .lg .middle } [__Components Guides__](components/index.md)
 
-## What is RAG and continual learning?
-### RAG
+    ---
 
-Retrieval augmented generation (RAG) is a paradigm for augmenting LLM with custom data. It generally consists of two stages:
+    Discover the various components used within Ragas.
+
+    Components like [Prompt Object](components/index.md#prompt-object), [Evaluation Dataset](components/index.md#evaluation-dataset) and [more..](components/index.md)
 
-- indexing stage: preparing a knowledge base, and
+-   ::material-ruler-square:{ .lg .middle } [__Ragas Metrics__](metrics/index.md)
 
-- querying stage: retrieving relevant context from the knowledge to assist the LLM in responding to a question
+    ---
 
-### Continual Learning
+    Explore available metrics and understand how they work.
 
-Continual learning is concept used in machine learning that aims to learn, iterate and improve ML pipelines over its lifetime using the insights derived from continuous stream of data points.  In LLM & RAGs, this can be applied by iterating and improving each components of LLM application from insights derived from production and feedback data.
+    Metrics for evaluating [RAG](metrics/index.md/#retrieval-augmented-generation), [Agentic workflows](metrics/index.md/#agents-or-tool-use-cases) and [more..](metrics/index.md/#list-of-available-metrics).
 
-<div class="grid cards" markdown>
+-   :material-database-plus:{ .lg .middle } [__Test Data Generation__](test_data_generation/index.md)
+
+    ---
+
+    Create high-quality datasets for comprehensive testing.
+
+    Algorithms for synthesizing data to test [RAG](test_data_generation/index.md#retrieval-augmented-generation), [Agentic workflows](test_data_generation/index.md#agents-or-tool-use-cases) 
 
-- [Evaluation Driven Development](evaluation_driven.md)
 
-    What is EDD?
+-   :material-chart-box-outline:{ .lg .middle } [__Feedback Intelligence__](feedback.md)
 
-- [Ragas Metrics](metrics/index.md)
+    ---
 
-    What metrics are available? How do they work?
+    Leverage signals from production data to gain actionable insights.
 
-- [Synthetic Test Data Generation](testset_generation.md)
+    Learn about to leveraging implicit and explicit signals from production data.
 
-    How to create more datasets to test on?
 
-- [Utilizing User Feedback](feedback.md)
 
-    How to leverage the signals from user to improve?
 
 </div>