Prompt-Engineering on Ghafoor's Personal Blog

Advanced Methods of Prompt Engineering

noreply@example.com (AG Sayyed) — Fri, 21 Nov 2025 02:15:23 +0000

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.

Zero-Shot Prompting

Zero-shot prompting instructs an LLM to perform a task without any prior specific training or examples. This method relies on the model’s pre-existing knowledge and understanding to respond to queries.

In-Context Learning

noreply@example.com (AG Sayyed) — Wed, 19 Nov 2025 18:27:45 +0000

This document explores in-context learning as a method where LLMs learn new tasks from demonstrations provided within prompts at inference time, without requiring additional training. The discussion covers prompt engineering fundamentals, including how to design effective prompts with clear instructions and context to guide AI systems toward generating accurate and relevant responses.

Understanding In-Context Learning

In-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language. This approach enables language models to perform new tasks without requiring traditional training processes.

Guidelines for Prompting

noreply@example.com (AG Sayyed) — Thu, 12 Dec 2024 09:34:43 +0000

This guide outlines two key principles for writing effective prompts for large language models (LLMs). By following these guidelines, you can craft clear and structured prompts that help the model generate accurate and relevant responses.

Guidelines for Prompting

Note: These notes are based on the videos available on the OpenAI platform.

This guide outlines two key principles for writing effective prompts for large language models (LLMs).

LLM Types

noreply@example.com (AG Sayyed) — Thu, 12 Dec 2024 03:53:21 +0000

Large Language Models (LLMs) are categorized into three main types based on their training and functionality. Each type has unique characteristics and use cases. This document provides an overview of the three types of LLMs and their applications.

Types of LLMs

There are three main types of LLMs:

Base LLM
Instruction-Tuned LLM
Instruction-and-Data-Tuned LLM

Type 1: Base LLM

Base LLMs are the simplest form of LLMs. They predict the next word based on patterns in the training data.