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🔍 𝟯 𝗞𝗲𝘆 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 𝗧𝗵𝗮𝘁 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗟𝗟𝗠 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 & 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗢𝘂𝗿 𝗔𝗜 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 While solo-building Zinkmail — my AI assistant that cleans up inboxes and surfaces what matters — I ran into a tricky problem: When I sent 50+ emails to the LLM in one batch, the summaries looked fine… but accuracy? Not so much Missing context. Misleading outputs. Hallucinations were creeping in — and that wasn’t okay So I started experimenting ✅ Broke the payload into smaller, meaningful chunks (5–10 emails per batch) ✅ Refined the system prompt to reinforce structure + tone ✅ Introduced consistency in formatting before inference After this redesign, summary quality improved by over 𝟲𝟬%, and hallucinations dropped significantly And none of this came from a course or tutorial This came from building, failing, debugging, and staying obsessed with solving the right problem It’s applied system thinking at the LLM layer