Up tókànItọsọna atẹle
Anthropic Claude Opus ati Sonnet Tiers
Awọn ile-iṣẹ
Awọn ile-iṣẹ Itọsọna
Anthropic dagbasoke awọn awoṣe Claude ati awọn ọja ti o ni ibatan fun ede, ifaminsi, onínọmbà, ati iṣẹ iranlọwọ irinṣẹ.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
The Claude application, developer API, and models offered through other platforms can have different features and controls. Evaluate the exact model and deployment surface used by the application.
Start with current model documentation and the corresponding system or model card. Read what was evaluated, under which conditions, and which limitations were reported. A provider’s broad quality description should not replace a task-specific evaluation. Separate generated text from tool execution. In a tool-using application, the model can request an operation, while the application or provider executes it and returns a result. The surrounding software must manage authorization, validation, retries, and completion checks. Check platform differences. Model identifiers, supported features, rate limits, and account controls can vary between the direct API and a cloud-platform integration. Keep the actual endpoint and model version in the evaluation record. Test representative work, including unsupported questions, long context, conflicting evidence, and failed tools. Review the applicable data controls before sending private material. Maintain a rollback and deprecation plan so a model change does not silently alter a production workflow.
04Apẹẹrẹ ṣiṣẹ
Imagine a Claude-based assistant answering a policy question after a retrieval tool fails.
Check whether the assistant clearly reports the missing evidence or invents a policy from general context.
Include the failure case in the application evaluation and verify the behavior after model or prompt changes.
Ohun ti o fihan
The constructed example assesses the deployed workflow rather than only normal model responses.
Turn system-card limitations into application-specific regression cases.
Verify an external action after a Claude tool request rather than trusting generated narration.
Ìwé ìròyìn ọfẹ
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
No. The application still needs authorization, input checks, reliable execution, and verification of the resulting state.
Tesiwaju kikọ
Awọn itọsọna diẹ sii ti a yan fun koko yii
Up tókànItọsọna atẹle
Anthropic Claude Opus ati Sonnet Tiers
Awọn ile-iṣẹ