The efficiency depends on the query size relative to the data distribution. A small query in a sparse region prunes almost everything. A query that covers the whole space prunes nothing (because every node overlaps), degenerating to a brute-force scan. The quadtree gives you the most benefit when your queries are spatially local, which is exactly the common case for map applications, game physics, and spatial databases.
The fourth tactic requires creating comparison tables and structured data that AI models can easily parse and reference. Language models excel at processing structured information organized in clear, consistent formats. When they encounter well-formatted comparison tables, step-by-step lists, or data organized in predictable structures, they can extract and cite that information more reliably than when similar content appears in dense paragraphs.
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