---
type: source
title: "3 MOST Important Prompt Engineering Techniques you NEED to know!"
description: "Effective prompt engineering comes down to three simple, high-leverage techniques (self-critique, role personification, and few-shot examples) rather than the dozens of overwrought methods people obsess over."
youtube_id: yo5liQr6Wy4
url: https://www.youtube.com/watch?v=yo5liQr6Wy4
slug: 3-most-important-prompt-engineering-techniques-you-need-to-know
published: 2023-05-28
duration: "0:10:46"
recency_rank: 193
raw: "../raw/3-most-important-prompt-engineering-techniques-you-need-to-know.md"
tags: [prompt-engineering, role-prompting, few-shot, self-criticism]
created: 2026-07-21
updated: 2026-07-21
---

# 3 MOST Important Prompt Engineering Techniques you NEED to know!

Cole frames prompt engineering as simply crafting your questions so the AI gives the answer you actually want, and argues that most people fall into one of two traps: knowing too little (and getting disappointing results) or over-researching dozens of techniques until writing a prompt takes longer than the answer is worth. His pitch is to keep it simple and rely on three techniques that cover nearly every use case.

The three techniques are: (1) have the AI criticize its own output, personifying it as a critic with specific quality requirements, then rewrite based on that critique; (2) personify the AI into a role that freely gives opinions, which overrides the model's tendency to punt with "it's subjective" and forces a direct answer; and (3) provide question/answer examples before your real question when you need a specific, parseable output format or a particular voice and tone. He identifies role personification as the overarching concept wrapping all of prompt engineering.

He also pushes back on commonly recommended techniques that add little value, singling out "think step by step" as unnecessary with strong models like GPT-4 that already reason that way by default. His broader point: many shared prompt templates only work on the cherry-picked examples their authors demo, so don't waste time implementing techniques that don't reliably improve the output.

## Concepts covered

- [Prompt Engineering](../concepts/prompt-engineering.md)
- [Adversarial Agents](../concepts/adversarial-agents.md)
- [Role Prompting](../concepts/role-prompting.md)
- [Effective Prompting](../concepts/effective-prompting.md)
- [Few-Shot Prompting](../concepts/few-shot-prompting.md)
- [Structured Outputs](../concepts/structured-outputs.md)
- [Reasoning Models](../concepts/reasoning-models.md)

## Entities

- [Cole Medin](../entities/people/cole-medin.md)
- [ChatGPT](../entities/tools/chatgpt.md)
- [GPT-4](../entities/tools/gpt-4.md)
- [OpenAI](../entities/organizations/openai.md)

## Key moments

- **[0:00:00]** Defines prompt engineering as crafting questions to get the answer you want; bad prompts give bad answers
- **[0:01:32]** Technique 1: have the AI criticize itself, personified as a tweet critic with specific requirements
- **[0:03:38]** Technique 2: personify the AI as a specialist who gives opinions to defeat "it's subjective" answers (car wrap color example)
- **[0:05:14]** Technique 3: provide question/answer examples to force a specific, parseable output format (math equation example)
- **[0:07:10]** Examples also help match a specific voice and tone for creative content like tweets
- **[0:08:00]** Overrated technique: telling GPT-4 to "think step by step" is unnecessary since it already does
- **[0:09:32]** Recap of all three techniques

## Transcript

[Raw transcript](../raw/3-most-important-prompt-engineering-techniques-you-need-to-know.md)
