The Ultimate Guide to Prompting GPT-5 for Better Results
Z
Zack Saadioui
8/13/2025
The Ultimate Guide to Prompting GPT-5 for Better Results
Hey everyone, hope you're doing awesome. So, the moment we’ve all been waiting for is here – GPT-5 has landed, & it's honestly a pretty big deal. If you've played around with it, you know it's a major step up from what we've seen before. But here’s the thing: just because the AI is smarter doesn't mean you automatically get better results. The magic, as always, is in how you talk to it.
I’ve been spending a TON of time in the trenches with GPT-5, figuring out what makes it tick & how to get the most out of it. Turns out, a lot of the old rules still apply, but there are some new tricks & nuances that can make a HUGE difference. This isn't just about getting it to write a cool poem or a funny email anymore. We're talking about using this thing to seriously level up your work, your business, & your creativity.
This is going to be a deep dive. We're talking ~3000 words of pure, actionable advice. We'll cover everything from the foundational principles that still work to advanced techniques that will make you look like a prompt engineering wizard. We'll also look at how to apply this stuff in the real world, especially for business. So grab a coffee, get comfortable, & let’s get into it.
The Core Philosophy: Shifting from "Tool" to "Co-Creator"
First things first, let's get our mindset right. The biggest mistake I see people make is treating GPT-5 like a glorified search engine or a simple command-line tool. You type something in, it spits something out. That’s old news.
The real power of GPT-5 comes when you start thinking of it as a collaborator or a co-creator. This isn't just a semantic difference; it fundamentally changes how you interact with it. A tool is passive; a co-creator is an active participant. You wouldn't just bark orders at a human collaborator, right? You'd provide context, give examples, & have a back-and-forth conversation. That’s EXACTLY how you need to approach GPT-5.
Think of it like this:
Vague Prompt (Tool mindset): "Write a report on customer satisfaction."
Collaborative Prompt (Co-Creator mindset): "Let's work on a 2-page report analyzing our Q3 customer satisfaction data. I want you to focus on three key areas: trends in product usage, regional performance variations, & common feedback themes about our support team. The goal is to identify 3-5 actionable recommendations for the leadership team. I've attached the raw survey data."
See the difference? The second prompt doesn't just give an order. It sets a scene, defines the players, outlines the goals, & provides the raw materials. It invites collaboration. This is the single most important shift you can make to improve your results.
The Unskippable Basics: Crafting a Rock-Solid Prompt
Before we get into the fancy stuff, we need to make sure our foundation is solid. These are the non-negotiables of good prompting. Skipping these is like trying to build a house without a foundation – it’s gonna crumble.
1. Be INSANELY Specific
If you remember one thing from this guide, make it this. Vague prompts lead to generic, unhelpful, & frankly, boring responses. The AI doesn't know your intentions, your audience, or your secret hopes & dreams. You have to spell it out.
Weak Prompt: "Write something about climate change."
Strong Prompt: "Write a 500-word explanation of how climate change is impacting marine ecosystems, specifically focusing on the phenomenon of coral bleaching. The target audience is high school students, so use clear, accessible language & include 3 specific, real-world examples of affected reefs. End with a short paragraph on potential solutions being explored by scientists."
The more details you provide, the less the AI has to guess. Think about:
Format: "Give me this as a bulleted list," "write it in a table," "format this as a JSON object."
Length: "In under 200 words," "a 3-paragraph summary," "a comprehensive, multi-page report."
Tone: "Use a friendly & casual tone," "write this in a formal, academic style," "make it witty & humorous."
Audience: "Explain this to a 5-year-old," "write for an audience of expert software developers," "this is for a marketing team."
2. Assign a Role or Persona
This is one of the easiest & most powerful tricks in the book. Giving the AI a persona instantly frames its response & helps it adopt the right tone, vocabulary, & perspective. It's like casting an actor in a role.
Without a Persona: "Explain the benefits of content marketing."
With a Persona: "You are a seasoned content marketing expert with 15 years of experience advising Fortune 500 companies. Explain the key benefits of a long-term content marketing strategy to a skeptical CFO. Focus on ROI, brand equity, & lead generation."
You can get SUPER creative with this. Some of my favorites:
"Act as a cynical, battle-hardened journalist..."
"You are a patient & encouraging teacher..."
"Take on the persona of a 1920s detective..."
"You are a world-class chef explaining a recipe..."
OpenAI has even started experimenting with built-in personas like "Cynic" & "Nerd," which just goes to show how effective this technique is.
3. Provide Context & Examples (Few-Shot Prompting)
This is where you really start to guide the AI's thinking. Instead of just telling it what to do, you show it. This is a technique called "few-shot prompting," & it’s incredibly effective for getting the AI to mimic a specific style or format.
Let's say you want to generate product descriptions in a very specific format.
Prompt:
"I need you to write product descriptions for my new line of smart home devices. You must follow the format I provide in the examples below.
Example 1:Product: SmartLight Bulb
Description: Effortlessly set the mood with over 16 million colors right from your phone. Our SmartLight Bulb connects directly to your Wi-Fi, no hub required. Schedule it, dim it, & even sync it with your music for the ultimate smart lighting experience.
Key Features:
16 Million Colors
Wi-Fi Connected (No Hub)
App & Voice Controlled
Example 2:Product: SmartPlug Mini
Description: Turn any appliance into a smart device with our compact SmartPlug Mini. Control lamps, fans, or coffee makers from anywhere in the world using our simple app. Set schedules & timers to automate your home & save energy.
Key Features:
Remote Control
Scheduling & Timers
Energy Monitoring
Now, generate a description for this new product:
Product: SmartCam Pro"
By providing examples, you're not just telling it what to do, you're giving it a template to follow. The results are DRAMATICALLY better & more consistent.
Going Pro: Advanced Prompt Engineering Techniques
Alright, you've mastered the basics. Now it's time to unlock the next level. These are the techniques that separate the amateurs from the pros & allow you to tackle truly complex tasks.
These sound complicated, but the core idea is simple: you force the AI to "think out loud" before giving you the final answer. This is especially useful for problems that require logic, reasoning, or multiple steps.
Humans rarely solve complex problems in one go. We break them down, work through the steps, & then arrive at a solution. CoT mimics this process.
Standard Prompt: "Liam has 40 marbles. He gives 7 marbles each to 4 of his friends. How many marbles does he have left?"
Chain-of-Thought Prompt: "Liam has 40 marbles. He gives 7 marbles each to 4 of his friends. How many marbles does he have left? Let's think step by step."
Just adding that simple phrase, "Let's think step by step," encourages the model to break down its reasoning:
First, figure out the total number of marbles given away.
Liam has 4 friends, & he gives each 7 marbles.
So, 4 friends * 7 marbles/friend = 28 marbles given away.
Liam started with 40 marbles.
Subtract the marbles given away from the starting amount: 40 - 28 = 12.
Therefore, Liam has 12 marbles left.
Tree-of-Thoughts (ToT) takes this a step further. Instead of just one line of reasoning, you ask the AI to explore multiple possibilities at each step, evaluate them, & then choose the best path forward. It's like creating a decision tree. This is fantastic for creative problem-solving or strategic planning.
ToT Prompt Example:
"We need to design a more sustainable coffee cup. Let's explore this using a Tree-of-Thoughts approach.
Step 1: Brainstorm three different initial design concepts for a sustainable coffee cup.
Step 2: For each concept, list the pros & cons.
Step 3: Based on the pros & cons, select the most promising concept & elaborate on its key features & materials."
This structured approach guides the AI through a much more rigorous creative process than a simple "design a sustainable coffee cup" prompt.
The ReAct Framework (Reason + Act)
This one is REALLY cool & essential for creating agents that can interact with the world. ReAct stands for Reason and Act. You prompt the model to cycle through a loop of:
Reasoning: Thinking about what it needs to do next.
Acting: Performing an action, like using a tool (e.g., searching the web, running code).
Observing: Seeing the result of that action.
Reasoning again: Using the new information to decide on the next step.
This is how you get GPT-5 to tackle complex, multi-step tasks that require external information. It's less about a single prompt & more about a system of prompting.
GPT-5's New Superpowers:
1
reasoning_effort
& the
1
Responses API
Okay, let's talk about the shiny new toys that come with GPT-5. These are specific parameters you can use via the API that give you unprecedented control.
Dialing in the Brainpower with
1
reasoning_effort
OpenAI gave us a new dial called
1
reasoning_effort
. This parameter literally controls how much "thinking" the model does before it gives you an answer. It has a few settings, but the most interesting one is
1
minimal
.
1
minimal
: This is for when you need speed. The model generates very few "reasoning tokens" & gives you an answer almost instantly. It's great for simple, latency-sensitive tasks like classification or quick chatbot responses.
1
medium
(the default): A good balance between speed & thoroughness.
1
high
: For when you need the model to do some deep thinking on a complex problem.
This is a game-changer because you can now choose the right tool for the job. You don't need PhD-level reasoning to answer a simple FAQ, & now you don't have to pay (in time or tokens) for it.
Getting Granular with
1
verbosity
Separate from
1
reasoning_effort
is the
1
verbosity
parameter. This controls how talkative the final answer is. You can have the model think really hard (
1
reasoning_effort: high
) but give you a very short answer (
1
verbosity: low
), or vice-versa. This is amazing for getting exactly the output you need without having to manually trim it down.