Large language models (LLMs) are revolutionizing how we interact with information, but they sometimes stumble over facts, generating what AI researchers call "hallucinations." Imagine an AI confidently telling you something completely false—that's a hallucination. Researchers are constantly working to ground these models in reality, and a new approach called WeKnow-RAG is showing real promise. This innovative system combines the power of web searches with the structured insights of knowledge graphs. Think of it as giving the LLM a toolbox filled with reliable resources to cross-check its answers. WeKnow-RAG doesn't just search the web randomly; it uses a clever multi-stage process. First, it quickly scans for potentially relevant information using keywords. Then, it dives deeper into the most promising results, using semantic similarity to find the best matches. Finally, the LLM double-checks its own work, rating its confidence in the generated answer. If it's not sure, it simply says, "I don't know," rather than making things up. This self-assessment feature is key to minimizing those pesky hallucinations. What sets WeKnow-RAG apart is its adaptability. It understands that different topics require different approaches. For instance, a question about a recent sporting event needs up-to-the-minute information from the web, while a historical query might be better answered by a knowledge graph. WeKnow-RAG intelligently switches between these resources depending on the question. The results? WeKnow-RAG is significantly boosting the accuracy of LLM responses and reducing hallucinations. While there are still challenges to overcome, this research represents a crucial step toward creating more trustworthy and reliable AI systems that can confidently navigate the complexities of the real world.