docs: update tutorials index page (#2190)

Minor formatting update -- since this section stands right now looks
quite bad

docs/docs/tutorials/index.md

@@ -31,14 +31,14 @@ Learn from example implementations of graphs designed for specific scenarios and
 
 #### RAG
 
-- [Adaptive RAG](rag/langgraph_adaptive_rag.ipynb)
-    - [Adaptive RAG using local LLMs](rag/langgraph_adaptive_rag_local.ipynb)
-- [Agentic RAG](rag/langgraph_agentic_rag.ipynb)
-- [Corrective RAG](rag/langgraph_crag.ipynb)
-    - [Corrective RAG using local LLMs](rag/langgraph_crag_local.ipynb)
-- [Self-RAG](rag/langgraph_self_rag.ipynb)
-    - [Self-RAG using local LLMs](rag/langgraph_self_rag_local.ipynb)
-- [SQL Agent](sql-agent.ipynb)
+- [Agentic RAG](rag/langgraph_agentic_rag.ipynb): Use an agent to figure out how to retrieve the most relevant information before using the retrieved information to answer the user's question.
+- [Adaptive RAG](rag/langgraph_adaptive_rag.ipynb): Adaptive RAG is a strategy for RAG that unites (1) query analysis with (2) active / self-corrective RAG. Implementation of: https://arxiv.org/abs/2403.14403
+    - For a version that uses a local LLM: [Adaptive RAG using local LLMs](rag/langgraph_adaptive_rag_local.ipynb)
+- [Corrective RAG](rag/langgraph_crag.ipynb): Uses an LLM to grade the quality of the retrieved information from the given source, and if the quality is low, it will try to retrieve the information from another source. Implementation of: https://arxiv.org/pdf/2401.15884.pdf 
+    - For a version that uses a local LLM: [Corrective RAG using local LLMs](rag/langgraph_crag_local.ipynb)
+- [Self-RAG](rag/langgraph_self_rag.ipynb): Self-RAG is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents and generations. Implementation of https://arxiv.org/abs/2310.11511.
+    - For a version that uses a local LLM: [Self-RAG using local LLMs](rag/langgraph_self_rag_local.ipynb) 
+- [SQL Agent](sql-agent.ipynb): Build a SQL agent that can answer questions about a SQL database.
 
 #### Planning Agents