A thread you can test
Four-Layer Hands-on Optimization
4 notes move from the word to a real choice at work — understand it first, then decide whether to use it.
Each note stands alone, or becomes the next step in this thread.
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTWhat is Four-Layer Hands-on Optimization, and which AI decisions does it change?
Bold ** alone eats 8.5% of Tokens; YAML for complex objects, CSV for flat lists, force Minified JSON on backend output This page keeps the related concepts, common mistakes, and practical notes in one reading thread.
First decide whether you are blocked by a definition, a choice, or verification; then choose the closest of the 4 notes below.
Start with “Syntax Layer: Prompts Are Written for Machines,” then restate the conclusion using your own task.
Do not treat every method in a topic as interchangeable. The answer changes with the input, risk, and acceptance bar.
THIS QUESTION THREAD
Put the word back inside the choice it changes.
Syntax Layer: Prompts Are Written for Machines
Bold ** alone eats 8.5% of Tokens; YAML for complex objects, CSV for flat lists, force Minified JSON on backend output
Semantic Layer: Double Distillation
Lost-in-the-middle: the more you stuff in, the less it holds onto what matters. Dynamic Few-Shot cuts 4000 to 500; LLMLingua-2 compresses 5–20×
Architecture Layer: KV Cache Caveats
Prefix matching saves up to 90%; why switching tools invalidates the entire cache; sliding window vs chapter cache
Output Layer: Keep the Model's Mouth Shut
Explicit negative constraints cut ~30% fluff; polish with Diff, don't rewrite the whole passage; use stop sequences as a hard cut