Use when designing production-LLM prompts — few-shot, chain-of-thought, system prompts, templates, self-verification — distinct from prompt-optimizer and refine-prompt.
日本語の概要は準備中です。原文の説明を表示しています。
29 件 ・ 関連度順
概要と使いどころ
Use when designing production-LLM prompts — few-shot, chain-of-thought, system prompts, templates, self-verification — distinct from prompt-optimizer and refine-prompt.
日本語の概要は準備中です。原文の説明を表示しています。
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati
日本語の概要は準備中です。原文の説明を表示しています。
Write effective prompts for LLMs — structure, few-shot examples, chain-of-thought, system prompts, and output parsing.
日本語の概要は準備中です。原文の説明を表示しています。
Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use when building AI features, improving agent performance, or crafting system prompts.
日本語の概要は準備中です。原文の説明を表示しています。
Declare an agentic application's interaction disclosure, state, context, capabilities, and effect-control boundaries. Use for a reviewable design specification and static consistency audit before implementation. NOT for proving that controls are deployed or enforced, exposing private chain-of-thought, selecting a model/router, or designing a multi-agent protocol.
日本語の概要は準備中です。原文の説明を表示しています。