Prompt Engineering on 中文AI术语词典https://terms-en.ai-term-hub.com/zh/tags/prompt-engineering/Recent content in Prompt Engineering on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000Token最大化https://terms-en.ai-term-hub.com/zh/terms/token_maxxing/Sat, 18 Jul 2026 11:37:02 +0000https://terms-en.ai-term-hub.com/zh/terms/token_maxxing/<h2 id="definition">Definition</h2> <p>Token最大化涉及精心构造输入内容，以充分利用模型的上下文窗口容量，或通过优化Token的语义密度来提升性能。实践者可能会填充无关文本以占满上下文空间（Padding），或者极度压缩信息以提高单位Token的信息量。这种策略常用于在有限的计算资源下获取更精确的回答，或在API调用成本受限的情况下最大化单次请求的输出价值。</p> <h3 id="summary">Summary</h3> <p>一种提示工程优化策略，旨在大型语言模型中最大化特定Token数量的效用或输出质量。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>提示工程</li> <li>上下文窗口</li> <li>语义密度</li> <li>优化</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>最大化LLM推理能力</li> <li>降低API使用成本</li> <li>测试模型边界</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E4%B8%8A%E4%B8%8B%E6%96%87%E9%95%BF%E5%BA%A6-context-length/">上下文长度 (Context Length)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%8F%90%E7%A4%BA%E6%B3%A8%E5%85%A5-prompt-injection/">提示注入 (Prompt Injection)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%88%86%E8%AF%8D-tokenization/">分词 (Tokenization)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%95%88%E7%8E%87%E8%B0%83%E4%BC%98-efficiency-tuning/">效率调优 (Efficiency Tuning)</a></li> </ul>思维链提示 (Chain-of-Thought Prompting)https://terms-en.ai-term-hub.com/zh/terms/chain_of_thought_prompting/Sat, 18 Jul 2026 10:59:27 +0000https://terms-en.ai-term-hub.com/zh/terms/chain_of_thought_prompting/<h2 id="definition">Definition</h2> <p>思维链（CoT）提示通过明确要求模型阐述其逐步逻辑，从而提升大型语言模型在复杂推理任务上的表现。与直接跳跃到最终答案不同，CoT引导模型展示其思考过程，这有助于解决需要多步逻辑推导的问题，如数学应用题或复杂的逻辑推理任务。</p> <h3 id="summary">Summary</h3> <p>思维链提示是一种鼓励大语言模型在生成最终答案之前，先生成中间推理步骤的技术。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>逐步推理</li> <li>少样本学习</li> <li>逻辑演绎</li> <li>中间步骤</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>解决数学应用题</li> <li>复杂逻辑推理任务</li> <li>调试代码生成错误</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span>prompt <span style="color:#f92672">=</span> <span style="color:#e6db74">&#34;Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. If each can has 3 balls, how many does he have?</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">A: Roger started with 5 balls. 2 cans of 3 balls each is 6 balls. 5 + 6 = 11. The answer is 11.&#34;</span> </span></span><span style="display:flex;"><span>print(prompt) </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/zero-shot-prompting-%E9%9B%B6%E6%A0%B7%E6%9C%AC%E6%8F%90%E7%A4%BA/">Zero-Shot Prompting (零样本提示)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/few-shot-prompting-%E5%B0%91%E6%A0%B7%E6%9C%AC%E6%8F%90%E7%A4%BA/">Few-Shot Prompting (少样本提示)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/self-consistency-%E8%87%AA%E6%B4%BD%E6%80%A7/">Self-Consistency (自洽性)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/reasoning-%E6%8E%A8%E7%90%86/">Reasoning (推理)</a></li> </ul>