prompting-claude-opus-4-8 changedbuild-with-claude/prompt-engineering/prompting-claude-opus-4-8
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9191## Design and frontend defaults
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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.
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 is a poor fit 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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142142## Code review harnesses
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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.
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 might initially see lower recall. This is likely a harness effect, not a capability regression. When a review prompt says things such as "only report high-severity issues," "be conservative," or "don't nitpick," Claude Opus 4.8 might follow that instruction more faithfully than earlier models did: it might 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.
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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.
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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."
154If you do want the model to self-filter in a single pass, be concrete about what the reporting threshold 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."
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156156Iterate on prompts against a subset of your evals or test cases to validate recall or F1 score gains.
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