Most AI workflow posts are just a screenshot of a chat box and a hopeful caption. This one is different: I ran the same local model twice on the same question , once with a raw prompt and once with a memory + retrieval stack around it. What changed Before : raw prompt no compression no semantic retrieval more clutter in context After : compressed working context semantic retrieval from memory notes fewer prompt tokens same model, same task, less nonsense The measured result From the proof pack: Before latency: 28,590.3 ms After latency: 25,008.9 ms Before accuracy: 0.500 After accuracy: 1.000 Before prompt tokens: 87 After prompt tokens: 108 Memory saved: -24.1% That last line is the fun one: the “after” run used more prompt tokens here, because I tuned it to answer the question better. Token count is a tool, not a religion. Why this matters The model did not become magical. The workflow got smarter.…