Prompting Claude Opus 5: Trim the Verbosity, Delete the Verification

Claude Opus 5 punishes prompt cruft. The instructions most teams carried over from earlier models, such as "double-check your answer," "include a final verification step," and "be thorough," now cause over-verification, longer outputs, and wasted tokens on a model that already checks its own work. This guide distills Anthropic's official Opus 5 prompting guide into what to add, and more importantly what to remove.

This is the model-specific layer. For techniques that apply across every current model, see our general prompt engineering playbook; for the other frontier models, see prompting Claude Fable 5 and prompting GPT-5.6.

Key Takeaways

• Prompt for conciseness explicitly. Opus 5's default responses run longer than prior Opus models, and effort controls thinking depth, not visible length.
• Delete verification instructions. The model verifies and self-corrects unprompted; explicit re-check instructions compound with that behavior and add cost with no quality gain.
• Constrain scope on narrow tasks. Opus 5 can expand tasks with unrequested steps; tell it to deliver what was asked at the scope intended.
• Cap subagent spawning. It delegates more readily than prior models, which multiplies cost on small tasks.
• Use low and medium effort liberally. Anthropic says they produce strong quality at a fraction of the tokens and latency; save xhigh for demanding coding and agentic work.
• Keep thinking enabled. Thinking at low effort usually beats thinking disabled at similar cost, and disabling it can leak tool calls as text or internal XML tags into output.
• Ask reviews to report everything. Opus 5 follows "only report high-severity issues" literally and finds less; filter severity in a separate pass instead.

Verbosity: The Lever Moved

The single biggest migration surprise: on Opus 5, the effort parameter controls how much the model thinks rather than how much it says, so lowering effort does not reliably shorten visible responses. Length is now a prompting problem. A short instruction ("keep responses focused, brief, and concise; spend the response on the main answer, not disclaimers") is effective, and in long system prompts Anthropic recommends pairing it with a one-line reminder near the end of the prompt.

The same explicit control applies to two adjacent behaviors. Opus 5 narrates agentic work readily, announcing what it is about to do; describe the cadence you want (one sentence before the first tool call, brief updates only at important findings, outcome-first summaries) to tune it down, or provide positive examples to tune it up. And files it writes to disk run longer than prior models' output, so if your product ships Claude-authored documents, add length calibration: cover the substance, no filler sections or boilerplate.

Delete Verification and Double-Check Instructions

Opus 5 verifies its own work and catches its own mistakes without being told. Anthropic's guidance is unambiguous: if your prompt contains explicit verification instructions or legacy harness scaffolding that adds separate verification steps, remove them, because they cause over-verification and waste tokens with no loss in quality when removed. The same goes for "double-check your answer" and "re-verify before responding."

One related behavior needs the opposite treatment. The model narrates corrections to its earlier statements more than prior models do. If that reads poorly in your product, scope it: only correct an earlier statement when the error would change the user's code or decisions, fix silent slips without noting them.

Scope, Subagents, and Review Prompts

Three behaviors are worth explicit guardrails:

The review point matters because code review is one of Opus 5's standout capabilities: Anthropic says it finds real bugs at a high rate per pass with mostly-real additional findings, and accuracy holds at lower effort. That supports a cheap fast pass at review time and a thorough pass later, but only if your prompt does not tell it to be conservative.

Effort and Thinking Settings

Start at the default (high) and adjust from your own evals. Anthropic's recommendation is to use low and medium liberally as the primary control for token cost and response time wherever quality holds, and to step up to xhigh for demanding coding and agentic work. If you carried effort defaults over from a prior model, re-run an effort sweep; the floor moved.

Thinking is on by default and can be disabled only at effort high or below. Prefer keeping it on: for most tasks, thinking enabled at low effort performs better than thinking disabled at similar cost. With thinking disabled, two artifacts occasionally appear: tool calls written as plain text that never execute (and then pollute the conversation history in agentic loops), and internal XML tags leaking into visible output. If you must run with thinking off, Anthropic's mitigation is a single combined instruction giving the model permission to speak briefly before tool calls, an out when no tool fits, and a general rule against internal tags. Avoid naming thinking tags specifically; the general form works better, and remove any "do not think" rules, which increase tag leakage.

Sources

• Anthropic, "Prompting Claude Opus 5": https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-opus-5
• Anthropic, "Prompting best practices": https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices