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.…