diff --git a/README.md b/README.md
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+++ b/README.md
@@ -352,10 +352,13 @@ With RAG, LLMs retrieves contextual documents from a database to improve the acc
 
 Real-life applications can require complex pipelines, including SQL or graph databases, as well as automatically selecting relevant tools and APIs. These advanced techniques can improve a baseline solution and provide additional features.
 
-* **Query construction**: Structured data stored in traditional databases requires a specific query language like SQL, Cypher, metadata, etc. We can directly translate the user instruction into a query to access the data with query construction.
+* **Query construction**: Structured data stored in traditional databases requires a specific query language like SQL, Cypher, metadata, etc. We can directly translate the user instruction into a query to access the data with query construction. 
 * **Agents and tools**: Agents augment LLMs by automatically selecting the most relevant tools to provide an answer. These tools can be as simple as using Google or Wikipedia, or more complex like a Python interpreter or Jira. 
-* **Post-processing**: Final step that processes the inputs that are fed to the LLM. It enhances the relevance and diversity of documents retrieved with re-ranking, [RAG-fusion](https://github.com/Raudaschl/rag-fusion), and classification.
+* **Post-processing**: Final step that processes the inputs that are fed to the LLM. It enhances the relevance and diversity of documents retrieved with re-ranking, [RAG-fusion](https://github.com/Raudaschl/rag-fusion), and classification. For more query structuring techniques, check out the Langchain course [RAG From Scratch](https://www.youtube.com/playlist?list=PLfaIDFEXuae2LXbO1_PKyVJiQ23ZztA0x). 
 * **Program LLMs**: Frameworks like [DSPy](https://github.com/stanfordnlp/dspy) allow you to optimize prompts and weights based on automated evaluations in a programmatic way.
+* **LLM routing**: Construct [adaptive RAG structure](https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_adaptive_rag.ipynb) with self-check for relevance, correctness, and hallucination. Powered with LangGraph for control and routing.
+* **Other Source of Retrieval**: RAG can also be powered with knowledge graphs using [Langchain](https://python.langchain.com/v0.1/docs/use_cases/graph/constructing/) or [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/query_engine/knowledge_graph_query_engine/).
+This is a sample from [LinkedIn Research](https://arxiv.org/html/2404.17723v1) using Knowledge Graph with RAG for customer support assistant. Additionally, Langchain also enables [querying SQL database](https://python.langchain.com/v0.2/docs/integrations/toolkits/sql_database/).
 
 📚 **References**:
 * [LangChain - Query Construction](https://blog.langchain.dev/query-construction/): Blog post about different types of query construction.
@@ -364,6 +367,8 @@ Real-life applications can require complex pipelines, including SQL or graph dat
 * [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) by Lilian Weng: More theoretical article about LLM agents.
 * [LangChain - OpenAI's RAG](https://blog.langchain.dev/applying-openai-rag/): Overview of the RAG strategies employed by OpenAI, including post-processing.
 * [DSPy in 8 Steps](https://dspy-docs.vercel.app/docs/building-blocks/solving_your_task): General-purpose guide to DSPy introducing modules, signatures, and optimizers.
+* [Langchain Adaptive RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag/): a strategy for RAG that unites (1) query analysis with (2) active / self-corrective RAG.
+* [Usage with Knowledge Graph](https://blog.langchain.dev/enhancing-rag-based-applications-accuracy-by-constructing-and-leveraging-knowledge-graphs/): Enhancing RAG-based application accuracy by constructing and leveraging knowledge graphs.
 
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 ### 5. Inference optimization