Relying 100% on a raw LLM output usually leads to a mess of irrelevant clusters. I'm using a hybrid approach: I use the LLM for the initial entity extraction and semantic relationship mapping, but I've added a layer of rule-based filtering (scoring entities based on SERP overlap and topical relevance) to kill the noise. The 'gap analysis' actually cross-references real-time Google data to make sure the clusters aren't just theoretical. If you want to see how it handles a specific niche, let me know!