<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Zero-Shot-Learning on Ghafoor's Personal Blog</title><link>http://ghafoorsblog.com/tags/zero-shot-learning/</link><description>Recent content in Zero-Shot-Learning on Ghafoor's Personal Blog</description><generator>Hugo</generator><language>en</language><managingEditor>noreply@example.com (AG Sayyed)</managingEditor><webMaster>noreply@example.com (AG Sayyed)</webMaster><copyright>Copyright © 2024-2026 AG Sayyed. All Rights Reserved.</copyright><lastBuildDate>Fri, 15 May 2026 13:20:20 +0100</lastBuildDate><atom:link href="http://ghafoorsblog.com/tags/zero-shot-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Advanced Methods of Prompt Engineering</title><link>http://ghafoorsblog.com/courses/ibm/rag-agentic-ai-content/rag-agentic-ai-pcert/01-develop-genai-apps/01-module/007-advanced-mentods/</link><pubDate>Fri, 21 Nov 2025 02:15:23 +0000</pubDate><author>noreply@example.com (AG Sayyed)</author><guid>http://ghafoorsblog.com/courses/ibm/rag-agentic-ai-content/rag-agentic-ai-pcert/01-develop-genai-apps/01-module/007-advanced-mentods/</guid><description>&lt;p class="lead text-primary"&gt;
This document examines advanced prompt engineering techniques that enhance LLM performance and reliability. It covers zero-shot, one-shot, and few-shot prompting methods, explores chain-of-thought reasoning and self-consistency approaches, and introduces essential tools like LangChain and prompt templates. The document also discusses practical applications of agents powered by LLMs for complex tasks across various domains.
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&lt;h2 id="zero-shot-prompting"&gt;Zero-Shot Prompting&lt;/h2&gt;
&lt;p&gt;Zero-shot prompting instructs an LLM to perform a task without any prior specific training or examples. This method relies on the model&amp;rsquo;s pre-existing knowledge and understanding to respond to queries.&lt;/p&gt;</description></item></channel></rss>