prompting-claude-opus-4-8 changedbuild-with-claude/prompt-engineering/prompting-claude-opus-4-8
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1515## Response length and verbosity
1616
17Claude Opus 4.8 calibrates response length to how complex it judges the task to be, rather than defaulting to a fixed verbosity. This usually means shorter answers on simple lookups and much longer ones on open-ended analysis.
17Claude Opus 4.8 calibrates response length to the complexity of the task, rather than defaulting to a fixed verbosity. This usually means shorter answers on simple lookups and much longer ones on open-ended analysis.
1818
19If your product depends on a certain style or verbosity of output, you may need to tune your prompts. As an example, to decrease verbosity, you might add:
19If your product depends on a certain style or verbosity of output, you might need to tune your prompts. As an example, to decrease verbosity, you might add:
2020
2121```text wrap
2222Provide concise, focused responses. Skip non-essential context, and keep examples minimal.
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2929The [effort parameter](https://platform.claude.com/docs/en/build-with-claude/effort) allows you to tune Claude's intelligence versus token spend, trading off capability for faster speed and lower costs. Start with the `xhigh` effort level for coding and agentic use cases, and use a minimum of `high` effort for most intelligence-sensitive use cases. Experiment with other effort levels to further tune token usage and intelligence:
3030
31* **`max`:** Max effort can deliver performance gains in some use cases, but may show diminishing returns from increased token usage. This setting can also sometimes be prone to overthinking. Test max effort for intelligence-demanding tasks.
31* **`max`:** Max effort can deliver performance gains in some use cases, but might show diminishing returns from increased token usage. This setting can also sometimes be prone to overthinking. Test max effort for intelligence-demanding tasks.
3232* **`xhigh`:** Extra high effort is the best setting for most coding and agentic use cases.
3333* **`high`:** This setting balances token usage and intelligence. For most intelligence-sensitive use cases, use a minimum of `high` effort.
3434* **`medium`:** Good for cost-sensitive use cases that need to reduce token usage while trading off intelligence.
3535* **`low`:** Reserve for short, scoped tasks and latency-sensitive workloads that are not intelligence-sensitive.
3636
37Claude Opus 4.8 respects effort levels strictly, especially at the low end. At `low` and `medium`, the model scopes its work to what was asked rather than going above and beyond. This is good for latency and cost, but on moderately complex tasks running at `low` effort there is some risk of under-thinking.
37Claude Opus 4.8 respects effort levels strictly, especially at the low end. At `low` and `medium`, the model scopes its work to what was asked rather than going beyond the request. This is good for latency and cost, but on moderately complex tasks running at `low` effort there is some risk of under-thinking.
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3939If you observe shallow reasoning on complex problems, raise effort to `high` or `xhigh` rather than prompting around it. If you need to keep effort at `low` for latency, add targeted guidance:
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4545Effort is likely to be more important for this model than for any prior Opus, so experiment with it actively when you upgrade.
4646
47On Claude Opus 4.8, thinking is off unless you explicitly set `thinking: {type: "adaptive"}`. The triggering behavior for [adaptive thinking](https://platform.claude.com/docs/en/build-with-claude/thinking) is steerable. If you find the model thinking more often than you'd like, which can happen with large or complex system prompts, add guidance to steer it. As always, measure the effect of any prompting changes on performance. Example:
47On Claude Opus 4.8, thinking is off unless you explicitly set `thinking: {type: "adaptive"}`. The triggering behavior for [adaptive thinking](https://platform.claude.com/docs/en/build-with-claude/thinking) is steerable. If you find the model thinking more often than you'd like, which can occur with large or complex system prompts, add guidance to steer it. As always, measure the effect of any prompting changes on performance. Example:
4848
4949```text wrap
5050Thinking adds latency and should only be used when it will meaningfully improve answer quality — typically for problems that require multistep reasoning. When in doubt, respond directly.
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7171## Tone and writing style
7272
73As with any new model, prose style on long-form writing may shift. Claude Opus 4.8 tends toward a direct, opinionated style with minimal validation-forward phrasing and sparing emoji use. If your product relies on a specific voice, re-evaluate style prompts against the new baseline.
73As with any new model, prose style on long-form writing might shift. Claude Opus 4.8 tends toward a direct, opinionated style with minimal validation-forward phrasing and sparing emoji use. If your product relies on a specific voice, re-evaluate style prompts against the new baseline.
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7575For instance, if your product voice is warmer or more conversational, add:
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9090
9191## Design and frontend defaults
9292
93Claude Opus 4.8 has strong design instincts, with a consistent default house style: warm cream/off-white backgrounds (\~`#F4F1EA`), serif display type (Georgia, Fraunces, Playfair), italic word-accents, and a terracotta/amber accent. This reads well for editorial, hospitality, and portfolio briefs, but will feel off for dashboards, dev tools, fintech, healthcare, or enterprise apps. The default appears in slide decks and web UIs.
93Claude Opus 4.8 has strong design instincts, with a consistent default house style: warm cream/off-white backgrounds (\~`#F4F1EA`), serif display type (Georgia, Fraunces, Playfair), italic word-accents, and a terracotta/amber accent. This reads well for editorial, hospitality, and portfolio briefs, but feels off for dashboards, dev tools, fintech, healthcare, or enterprise apps. The default appears in slide decks and web UIs.
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9595This default is persistent. Generic instructions ("don't use cream," "make it clean and minimal") tend to shift the model to a different fixed palette rather than producing variety. Two approaches work reliably:
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125125Before building, propose 4 distinct visual directions tailored to this brief (each as: bg hex / accent hex / typeface — one-line rationale). Ask the user to pick one, then implement only that direction.
126126```
127127
128Additionally, Claude Opus 4.8 requires less frontend design prompting than previous models to avoid generic patterns that users call the "AI slop" aesthetic. With earlier models, Anthropic recommended a lengthier prompt snippet in the [frontend-design skill](https://github.com/anthropics/claude-code/blob/main/plugins/frontend-design/skills/frontend-design/SKILL.md). However, Claude Opus 4.8 generates distinctive, creative frontends with more minimal prompting guidance. This prompt snippet works well with the preceding prompting advice for variety:
128Additionally, Claude Opus 4.8 requires less frontend design prompting than previous models to avoid generic patterns that users call the "AI slop" aesthetic. With earlier models, Anthropic recommended a lengthier prompt snippet in the [frontend-design skill](https://github.com/anthropics/claude-code/blob/main/plugins/frontend-design/skills/frontend-design/SKILL.md). However, Claude Opus 4.8 generates distinctive, creative front ends with more minimal prompting guidance. This prompt snippet works well with the preceding prompting advice for variety:
129129
130130```text wrap
131131<frontend_aesthetics>
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135135
136136## Interactive coding products
137137
138Claude Opus 4.8's token usage and behavior can differ between autonomous, asynchronous coding agents with a single user turn and interactive, synchronous coding agents with multiple user turns. Specifically, it tends to use more tokens in interactive settings, primarily because it reasons more after user turns. This can improve long-horizon coherence, instruction following, and coding capabilities in long, interactive coding sessions, but also comes with more token usage. To maximize both performance and token efficiency in coding products, use `xhigh` or `high` effort, add autonomous features like an auto mode, and reduce the number of human interactions required from your users.
138Claude Opus 4.8's token usage and behavior can differ between autonomous, asynchronous coding agents with a single user turn and interactive, synchronous coding agents with multiple user turns. Specifically, it tends to use more tokens in interactive settings, primarily because it reasons more after user turns. This can improve long-horizon coherence, instruction following, and coding capabilities in long, interactive coding sessions, but also comes with more token usage. To maximize both performance and token efficiency in coding products, use `xhigh` or `high` effort, add autonomous features such as an auto mode, and reduce the number of human interactions required from your users.
139139
140140Of course, when limiting the number of required user interactions, it's important to specify the task, intent, and relevant constraints upfront in the first human turn. Providing well-specified, clear, and accurate task descriptions upfront can help maximize autonomy and intelligence while minimizing extra token usage after user turns. Because Claude Opus 4.8 is more autonomous than prior models, this usage pattern helps to maximize performance. In contrast, ambiguous or underspecified prompts conveyed progressively over multiple user turns tend to relatively reduce token efficiency and sometimes performance.
141141
142142## Code review harnesses
143143
144Claude Opus 4.8 is meaningfully better at finding bugs than prior models, and has both higher recall and precision in internal evals. However, if your code-review harness was tuned for an earlier model, you may initially see lower recall. This is likely a harness effect, not a capability regression. When a review prompt says things like "only report high-severity issues," "be conservative," or "don't nitpick," Claude Opus 4.8 may follow that instruction more faithfully than earlier models did: it may investigate the code just as thoroughly, identify the bugs, and then not report findings it judges to be below your stated bar. This can show up as the model doing the same depth of investigation but converting fewer investigations into reported findings, especially on lower-severity bugs. Precision typically rises, but measured recall can fall even though the model's underlying bug-finding ability has improved.
144Claude Opus 4.8 is meaningfully better at finding bugs than prior models, and has both higher recall and precision in internal evals. However, if your code-review harness was tuned for an earlier model, you may initially see lower recall. This is likely a harness effect, not a capability regression. When a review prompt says things like "only report high-severity issues," "be conservative," or "don't nitpick," Claude Opus 4.8 may follow that instruction more faithfully than earlier models did: it may investigate the code just as thoroughly, identify the bugs, and then not report findings it determines to be below your stated bar. This can show up as the model doing the same depth of investigation but converting fewer investigations into reported findings, especially on lower-severity bugs. Precision typically rises, but measured recall can fall even though the model's underlying bug-finding ability has improved.
145145
146146Some recommended prompt language:
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152152This prompt can be used without having an actual second step, but moving confidence filtering out of the finding step often helps. If your harness has a separate verification, deduplication, or ranking stage, tell the model explicitly that its job at the finding stage is coverage rather than filtering.
153153
154If you do want the model to self-filter in a single pass, be concrete about where the bar is rather than using qualitative terms like "important": for example, "report any bugs that could cause incorrect behavior, a test failure, or a misleading result; only omit nits like pure style or naming preferences."
154If you do want the model to self-filter in a single pass, be concrete about where the bar is rather than using qualitative terms such as "important": for example, "report any bugs that could cause incorrect behavior, a test failure, or a misleading result; only omit nits like pure style or naming preferences."
155155
156156Iterate on prompts against a subset of your evals or test cases to validate recall or F1 score gains.
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