The short version

A prompt can become a collection of patches for yesterday’s model. Repeated checking, blanket searches, rigid reasoning templates, and stale examples may continue to consume time even after the underlying weakness has changed.

Anthropic’s platform guidance connects instruction maintenance with cost and performance. The practical lesson is to ask what each instruction still earns, rather than assume that a longer prompt is more dependable.

Requirements versus rituals

A requirement defines an outcome or a genuine process obligation. A ritual prescribes extra work without demonstrating that it improves the result.

“Every conclusion must have supporting evidence” is a quality requirement. “Check every conclusion twice” is a procedure that may or may not help. A mandatory business step should remain even when it costs tokens. An old workaround should be tested against the current model.

Review one instruction at a time

Classify the prompt’s instructions into three groups:

  • Essential: current product requirements, authority limits, evidence standards, and safety boundaries.
  • Conditional: extra investigation that is useful when uncertainty or contradiction matters.
  • Unproven: inherited emphasis, duplicated checks, stale examples, and procedures with no current failure attached.

Change a small group at a time. Keep the original prompt as a comparison point so a shorter version can be evaluated rather than admired merely for being shorter.

Replace intensity with a target

Consider a research prompt that asks for maximum thoroughness and repeated searches. A better contract can identify authoritative sources, the claims that need corroboration, how to disclose conflicting evidence, and when enough information has been gathered.

This gives Claude a destination and a stop condition. It leaves room for useful investigation without requiring the same amount of work on every task.

Measure the full trade-off

Compare revised and original prompts on the same representative cases. Look at task success, important errors, tool calls, response time, and cost. Include edge cases that motivated the old instructions; otherwise the experiment may miss a regression.

Keep safety boundaries separate from stylistic simplification. A lower token count is not a reason to remove authorization checks or evidence requirements.

Practical checklist

  • Attach each instruction to a current requirement or observed failure.
  • Replace blanket effort with decision-relevant triggers.
  • Remove contradictions before adding new rules.
  • Update examples when the model or task changes.
  • Compare both versions on the same cases.
  • Retain a rollback point.

Try it

Choose five inherited instructions. For each, name the failure it addresses and the test that would show whether it still helps. Remove only those that fail to justify their place.