Master Prompting with ChatGPT: Pt 1 - The Basics
This is the first article in a blog series where I’ll break down the art and science of prompting, one pattern at a time. Every installment from here on will zoom in on a unique prompt pattern, pulling it apart thread by thread, and giving you the nitty-gritty on how to maximize your ChatGPT use.
So, strap in and make sure those laces are tight. Whether you're a prompting pro or a fresh-faced newbie, this series promises to elevate your game.
Welcome to Prompt-Palooza! Hold onto your butts.
What is Prompting
Think of prompts as the Cane’s sauce that turns bland, unseasoned chicken tenders into your latest addiction.
LLMs like ChatGPT have crazy language generation capabilities. But effectively using these models requires using the right inputs, or prompts - instructions that establish rules, enforce constraints, and customize outputs.
Without them, the answers will be blander than that chicken without the secret sauce.
Well-constructed prompts act as programs that extend an LLM's skills. They can specify formatting, style guidelines, interaction norms, and content requirements.
As prompt engineering matures, patterns emerge for solving common challenges. Prompt patterns document proven techniques to save users time and aid prompt reuse.
The Beauty of Prompt Patterns
In the vast ocean of prompt engineering, prompt patterns are the lighthouse that guides you, only without Willem Dafoe as a bunkmate.
They're the tried-and-tested formulas that help customize LLM outputs. Think of them as the blueprints for building epic conversations with ChatGPT.
This series will be your guidebook, exploring each prompt pattern in detail. We'll delve into their intricacies, from input semantics to output customization, ensuring you're equipped to craft masterpieces with ChatGPT.
There are six basic classifications for the various prompt patterns we’ll review.
1. Input Semantics
The Input Semantics category helps an LLM process and understand what's given to it. A key part of this is the Meta Language Creation pattern, which creates a shared shorthand between the user and the LLM. This is like making a special language, especially when the usual way of talking to it doesn't quite get the point across.
2. Output Customization
The Output Customization category shapes how the LLM's answers look and feel. The patterns in this category are:
Output Automater: Lets users make scripts to do tasks based on the LLM's suggestions.
Persona: Makes the LLM act like a certain character or role when answering.
Visualization Generator: Helps users create visuals by giving text answers that can be turned into images with tools like DALL-E.
Recipe: Gives users a step-by-step guide to achieve a goal, even if some details are missing.
Template: Users set a format, and the LLM fills in the details.
In short, it's all about customizing how the LLM responds to you.
3. Error Identification
The Error Identification category is all about spotting and fixing mistakes in the LLM's answers. There are two main patterns here:
Fact Check List: This asks the LLM to list down facts in its answer that need to be double-checked for accuracy.
Reflection: This makes the LLM think about its own answer and point out any mistakes it might have made.
These patterns make sure the LLM's answers are on point.
4. Prompt Improvement
This category makes both the questions you ask and the answers you get better. Here's a breakdown of the patterns in this category:
Question Refinement: Helps the LLM suggest a clearer version of your question.
Alternative Approaches: The LLM offers different ways to do something you ask.
Cognitive Verifier: The LLM asks you smaller questions first, then combines your answers to reply to your main question.
Refusal Breaker: If the LLM can't answer, it'll try to rephrase your question to give it another shot.
This Prompt Improvement category enhances the conversation with the LLM.
5. Interaction
The Interaction category dictates how you and the LLM chat with each other. It's all about mixing up the way you and the LLM communicate. Here's the lowdown on the patterns:
Flipped Interaction: Instead of giving answers, the LLM asks you questions.
Game Play: The LLM turns its answers into a game format.
Infinite Generation: The LLM keeps giving answers without you needing to ask again and again.
6. Context Control
The Context Control category guides the background info the LLM uses. It gives the LLM the right setting to base its answers on. The main pattern here is Context Manager, which lets you set the scene or background for the LLM's answers.
Conclusion
Prompt patterns make prompt engineering more accessible by documenting community knowledge. Novices can apply patterns to boost prompt quality. Experts can rapidly share new techniques as patterns. Teams can align on vocabularies and approaches.
Here’s why you should become a prompt pattern expert:
They're time-savers. No need to reinvent the wheel.
Quality? Through the roof!
You don’t need a PhD. The heavy lifting is already done.
Mix and match to craft the perfect prompt.
Teamwork makes the dream work. Everyone's on the same page.
Customizable? You know it. Adapt them to any domain.
Stay tuned as we dive deeper into each of these patterns and how you can operationalize them to save you time and money.
Source
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
