- Published on
Prompting Claude Opus 5.5
- Authors
- Name
- DeepHorus
Anthropic has published a dedicated prompting guide for Claude Opus 5.5, focused on the behavioral differences developers may encounter when upgrading from Claude Opus 5. The company says existing Opus 5 prompts should still work, while its earlier prompting patterns remain a reasonable baseline. The document organizes recommendations around observed problems such as excessive effort, slow chat responses, incomplete autonomous runs, safeguard refusals, missed visual details, and generic frontend output. It also addresses multi-app discovery, multiagent timing, progress updates, and pasted user text.
Claude Opus 5.5 is positioned as a faster and more efficient successor, generating output tokens more than 30 percent faster than Claude Opus 5 and often completing the same task with fewer tokens. That efficiency makes effort calibration especially important for applications where previous prompts suddenly become slower or more expensive. In Anthropic’s testing, Opus 5.5 at its default medium effort matched or exceeded Claude Opus 5 at high effort on multistep coding and repository tasks, using fewer steps and tokens. Early testers also reported stronger code review, catching more bugs while producing fewer false alarms.
The guide recommends selecting an effort level based on task complexity, latency requirements, and cost targets rather than automatically using the highest setting. Integrations that previously disabled thinking should use prompts designed for thinking-disabled operation instead of assuming that extended reasoning is always active. In interactive chat applications, Anthropic suggests adding system-prompt instructions that produce a brief initial acknowledgment before longer work begins. Regular, milestone-based progress updates can also prevent users from mistaking a lengthy agentic turn for a stalled request.
Unattended agents require especially explicit persistence and completion criteria. Claude Opus 5.5 can sustain multi-hour audits, migrations, and other autonomous repository work, but the guide warns that an agent may still stop after reporting progress unless the prompt clearly distinguishes status updates from the final deliverable. Multi-app workflows should encourage the model to inspect relevant connected resources instead of relying only on information named in the request. For teams of agents, developers can provide time signals and deadlines that help the harness prioritize work and finish sooner without sacrificing oversight.
Safety behavior is another major focus. API applications that receive a stop_reason of "refusal" should inspect the request and determine which safeguard was triggered rather than blindly retrying it. The guide also recommends clearly marking text pasted by users as untrusted content and instructing Claude not to follow instructions found inside it. This is particularly important when external documents, transcripts, or web content could contain prompt-injection attempts. These patterns reinforce the instruction hierarchy instead of treating all text in a message as an equally authoritative command.
Claude Opus 5.5 also receives prompting guidance for visual and design workloads. Anthropic says the model can read dense charts, diagrams, and screenshots more accurately than Claude Opus 5, with its lowest effort setting reportedly outperforming the earlier model at its highest setting while using a fraction of the output tokens. Complex visual inputs may still benefit from tools that crop, enlarge, or otherwise preprocess important regions. For frontend development, prompts should specify concrete design direction, interaction details, and visual constraints, since the default output can otherwise look generic despite the model’s improved capabilities.
The immediate impact is likely lower latency and cost for existing applications, rather than a disruptive migration cycle, although agent builders may gain better results by revising effort, progress, and context-discovery instructions. Anthropic’s testing and early tester feedback also indicate fewer factual and citation errors, stronger financial modeling, and less editing for spreadsheets, presentations, and documents. Looking ahead, the guide frames prompting as an evolving harness discipline: teams will need to calibrate computation, define checkpoints, establish safety boundaries, and adapt interfaces as models take on longer and more consequential tasks.
