The field of artificial intelligence is moving towards more advanced and efficient methods for causal discovery and knowledge graph construction. Researchers are leveraging large language models (LLMs) and retrieval-augmented generation techniques to improve the accuracy and scalability of these methods. The use of LLMs is allowing for more efficient causal graph construction, while retrieval-augmented generation is enabling the incorporation of external knowledge into the process. This is leading to more accurate and interpretable results, and is being applied to a range of domains, including natural language processing and computer vision. Notable papers in this area include: Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring, which introduces a framework for fairness-driven causal discovery using LLMs. CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation, which proposes a novel framework that incorporates causal graphs into the retrieval process. SLIDE: Sliding Localized Information for Document Extraction, which introduces a chunking method that processes long documents by generating local context through overlapping windows.