<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Prompt Engineering on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/prompt-engineering/</link><description>Recent content in Prompt Engineering on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/prompt-engineering/index.xml" rel="self" type="application/rss+xml"/><item><title>Token maxxing</title><link>https://terms-en.ai-term-hub.com/en/terms/token_maxxing/</link><pubDate>Sat, 18 Jul 2026 10:18:37 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/token_maxxing/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Token maxxing involves carefully crafting inputs to utilize the full capacity of a model&amp;rsquo;s context window or to optimize the semantic density of tokens for better performance. Practitioners may pad prompts with irrelevant text to test limits or structure queries to ensure critical information fits precisely within token constraints. This technique is often used in competitive prompt engineering or when working with models that have strict input/output length limitations, ensuring no potential reasoning space is wasted.&lt;/p></description></item><item><title>Chain-of-Thought Prompting</title><link>https://terms-en.ai-term-hub.com/en/terms/chain_of_thought_prompting/</link><pubDate>Sat, 18 Jul 2026 09:40:12 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/chain_of_thought_prompting/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Chain-of-Thought (CoT) prompting improves the performance of large language models on complex reasoning tasks by explicitly asking the model to articulate its step-by-step logic. Instead of jumping directly to a conclusion, the model generates intermediate sentences that represent its thought process. This approach mimics human problem-solving strategies, significantly enhancing accuracy in mathematics, logic puzzles, and multi-step deductions. It can be implemented via few-shot examples or simple instructions like &amp;lsquo;Let&amp;rsquo;s think step by step.&amp;rsquo;&lt;/p></description></item></channel></rss>