Original Summary

​ How much context can you remove before your AI app gets worse? I’m the founder of Jylus. We tested three evidence paths using the same Gemini 3.1 Flash Lite settings and deterministic scorer. Results — average input tokens / strict accuracy: • Full context: 205,129 tokens / 78.79% • BM25-only RAG: 8,128 tokens / 76.14% • Jylus Context Pack: 2,532 tokens / 100% observed The BM25 result matters: it reduced input substantially, but accuracy was lower than full context. Sending fewer tokens alone wasn’t enough. Jylus prepares source-backed evidence before the model answers, resolving state and relationships and retaining conflicts or gaps where relevant. Your existing model then reasons over that Context Pack. This was our frozen adversarial benchmark across four data domains. It has not been independently reproduced, and 100% on these questions is not a universal accuracy claim. Methodology: https://jylus.ai/benchmark-methodology You can inspect the evidence Jylus returns using your own synthetic records, without an account: https://jylus.ai/try For people running AI apps: when you reduce context, what breaks first—missing facts, lost relationships, or incorrect state?   submitted by   /u/jylusdev [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/5 18:57:12