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7 changes: 4 additions & 3 deletions modules/genai-ecosystem/pages/index.adoc
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Expand Up @@ -17,16 +17,17 @@ https://neo4j.com/blog/genai/what-is-graphrag[GraphRAG (retrieval-augmented gene
== How to Get Started

1. For a high level overview, have a look at https://neo4j.com/generativeai/[neo4j.com/genai^]
2. Use the https://neo4j.com/labs/genai-ecosystem/llm-graph-builder[LLM-Knowledge Graph Builder^] to turn your own documents into a knowledge graph or see the example projects below for building graphs from mixed structured & unstructured data.
3. If you want to learn more take one of the https://graphacademy.neo4j.com/categories/llms/?ref=genai-docs[GenAI GraphAcademy courses^]
4. Pick your https://neo4j.com/labs/genai-ecosystem/genai-frameworks[GenAI framework of choice^] and start building your own GenAI applications with Neo4j
2. To build a knowledge graph from unstructured data, such as text documents, see xref:importing-graph-from-unstructured-data.adoc[Creating Graphs From Unstructured Data]. You can also experiment with the https://neo4j.com/labs/genai-ecosystem/llm-graph-builder[LLM-Knowledge Graph Builder^], a reference UI implementation built on top of Neo4j and LangChain integrations.
3. If you want to learn more, take one of the https://graphacademy.neo4j.com/categories/llms/?ref=genai-docs[GenAI GraphAcademy courses^] or have a look at the below <<example-projects>>
4. Pick your https://neo4j.com/labs/genai-ecosystem/genai-frameworks[GenAI framework of choice^] and start building your own GenAI applications with Neo4j.

== GraphAcademy Courses

If you want to learn how LLMs and Knowledge Graphs combine to improve GenAI applications, check out the https://graphacademy.neo4j.com/categories/llms/?ref=genai-docs[Neo4j & LLM courses on GraphAcademy^].

image::https://cdn.graphacademy.neo4j.com/assets/img/courses/banners/llm-fundamentals.png[link=https://graphacademy.neo4j.com/categories/llms/?ref=genai-docs]

[[example-projects]]
== Example Projects & Use Case Applications
Explore these projects to see how GraphRAG is applied in GenAI & agent workflows, various use cases, and integrations with specific cloud service providers.

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