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Knowledge Graphs & LLMs: Fine-Tuning Vs. Retrieval-Augmented Generation

Learn about the limitations of Large Language Models (LLMs) and how to overcome them using knowledge graphs and retrieval-augmented generation. In this blog post, Tomaz Bratanic explains how Neo4js NaLLM project is exploring practical uses of LLMs with Neo4j and constructing demonstrations in a GitHub repository. One major issue with LLMs is their knowledge cutoff, making them unaware of any events that happened after their training. Additionally, LLMs might produce inaccurate information, making it crucial to verify their responses, especially for missing data. Read the full blog post to learn more.