The highest-return prompt work you can do this week is not adding anything. It's deleting.

On July 9, 2026, OpenAI shipped its official prompting guide for the GPT-5.6 family (the flagship is codenamed Sol, with Terra and Luna as the price/performance and high-volume siblings). The guide reverses advice a lot of us internalized over the last two years. The old GPT-5 playbook told you to restate important rules, add "keep going until done" persistence blocks, and bolt on brevity constraints. On GPT-5.6, that same scaffolding measurably lowers output quality.

The single strongest recommendation in the guide: state each instruction exactly once. Then delete the duplicates, the redundant examples, the persistence padding, and the inherited "be brief" rules. This post shows you how to run that subtraction pass without breaking your agent.

The numbers, stated honestly

OpenAI's internal coding-agent evals found that swapping long explicit system prompts for lean ones improved evaluation scores by roughly 10 to 15% while cutting total tokens by 41 to 66% and cost by 33 to 67%.

Read that caveat before you get excited. OpenAI labels these figures "directional." They vary by workload, and the guide asks you to validate on your own representative tasks. These are not guarantees you can quote at a stakeholder meeting. They are a strong signal that the shape of a good prompt has changed.

Why redundancy went from helpful to harmful

Older models were sloppy instruction-followers. You repeated a rule in three places because that was the only way to make it stick. GPT-5.6 follows what the guide calls "prompt contracts" literally. When two rules overlap or quietly contradict each other, the model burns reasoning tokens trying to reconcile both. The result is slower, more expensive, and often wrong.

The guide puts it plainly: "conflicting rules can create more instability than missing detail."

So the duplicated instruction you added as a safety net is now the thing making your agent hesitate. Repetition reads as a second, slightly different rule, and the model tries to honor both.

What the subtraction pass looks like

Here is a bloated system prompt of the kind that was normal in 2025. Every line felt responsible when it was written.

You are a customer support agent. Be concise. Keep responses brief.
Resolve the customer's issue. Always be helpful and thorough.
Do not stop until the issue is fully resolved. Keep going until done.
Before taking any action that changes data, ask the customer first.
Never mutate account data without permission. Always confirm before writing.
Limit responses to three sentences. Be efficient and to the point.
If you are unsure, ask. Do not guess. When uncertain, request clarification.
Resolve the issue end to end and do not give up early.

Count the collisions. "Be concise," "keep responses brief," "be efficient," and "limit to three sentences" are four versions of one idea, and the three-sentence cap will truncate a legitimately complex answer. "Ask before you change data" appears three times, which on GPT-5.6 can trigger permission checks even for safe, expected actions. "Keep going until done" appears twice, and Sol is already persistent by default.

Now the lean rewrite. You can produce it by hand, or you can hand the old prompt to the model and ask it to do the cut for you.

The Prompt: ``` You are a prompt editor. Below is a system prompt for a GPT-5.6 agent.

Rewrite it so that: - Each distinct instruction appears exactly once. - Remove duplicated rules, restated brevity constraints, and any "keep going until done" persistence padding. - Keep every stopping condition, success criterion, and hard authorization boundary intact, stated once and clearly. - Do not add new rules. Only remove, merge, or tighten existing ones.

Return the rewritten prompt, then a short list of what you removed and why, so I can confirm nothing load-bearing was cut.

token reduction

GPT-5.6 Pays You to Delete Your System Prompt: State Every Rule Once and Stop Repeating Yourself