docs: reword framework definition (#17498)

docs/docs/index.md

@@ -2,7 +2,7 @@
 
 # Welcome to LlamaIndex 🦙 !
 
-LlamaIndex is a framework for building context-augmented generative AI applications with [LLMs](https://en.wikipedia.org/wiki/Large_language_model) including [agents](./understanding/agent/basic_agent/) and [workflows](./understanding/workflows/).
+LlamaIndex is the leading framework for building LLM-powered agents over your data with [LLMs](https://en.wikipedia.org/wiki/Large_language_model) and [workflows](./understanding/workflows/).
 
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@@ -34,14 +34,6 @@ LlamaIndex is a framework for building context-augmented generative AI applicati
 
 ## Introduction
 
-### What is context augmentation?
-
-LLMs offer a natural language interface between humans and data. LLMs come pre-trained on huge amounts of publicly available data, but they are not trained on **your** data. Your data may be private or specific to the problem you're trying to solve. It's behind APIs, in SQL databases, or trapped in PDFs and slide decks.
-
-Context augmentation makes your data available to the LLM to solve the problem at hand. LlamaIndex provides the tools to build any of context-augmentation use case, from prototype to production. Our tools allow you to ingest, parse, index and process your data and quickly implement complex query workflows combining data access with LLM prompting.
-
-The most popular example of context-augmentation is [Retrieval-Augmented Generation or RAG](./getting_started/concepts.md), which combines context with LLMs at inference time.
-
 ### What are agents?
 
 [Agents](./understanding/agent/basic_agent/) are LLM-powered knowledge assistants that use tools to perform tasks like research, data extraction, and more. Agents range from simple question-answering to being able to sense, decide and take actions in order to complete tasks.
@@ -52,6 +44,14 @@ LlamaIndex provides a framework for building agents including the ability to use
 
 [Workflows](./understanding/workflows/) are multi-step processes that combine one or more agents, data connectors, and other tools to complete a task. They are event-driven software that allows you to combine RAG data sources and multiple agents to create a complex application that can perform a wide variety of tasks with reflection, error-correction, and other hallmarks of advanced LLM applications. You can then [deploy these agentic workflows](./module_guides/workflow/deployment.md) as production microservices.
 
+### What is context augmentation?
+
+LLMs offer a natural language interface between humans and data. LLMs come pre-trained on huge amounts of publicly available data, but they are not trained on **your** data. Your data may be private or specific to the problem you're trying to solve. It's behind APIs, in SQL databases, or trapped in PDFs and slide decks.
+
+Context augmentation makes your data available to the LLM to solve the problem at hand. LlamaIndex provides the tools to build any of context-augmentation use case, from prototype to production. Our tools allow you to ingest, parse, index and process your data and quickly implement complex query workflows combining data access with LLM prompting.
+
+The most popular example of context-augmentation is [Retrieval-Augmented Generation or RAG](./getting_started/concepts.md), which combines context with LLMs at inference time.
+
 ### LlamaIndex is the framework for Context-Augmented LLM Applications
 
 LlamaIndex imposes no restriction on how you use LLMs. You can use LLMs as auto-complete, chatbots, agents, and more. It just makes using them easier. We provide tools like: