How to Prompt GPT-5: The Future of AI Prompting

8/12/2025

Say Goodbye to Basic Prompts: Here’s How You’ll Need to Prompt GPT-5

Alright, let's talk about something that's on every AI enthusiast's mind: what comes next? We've all gotten pretty good at talking to GPT-3 & GPT-4. We've learned to sweet-talk them, give them examples, & basically hold their hand to get the results we want. But here's the thing, the game is about to change. With a model like GPT-5 (or whatever they decide to call the next big thing), our old tricks just aren't going to cut it.

Honestly, prompting is becoming less of a simple instruction & more of a strategic conversation. It’s an art & a science. If you've been in this space for a while, you've seen the shift. We went from simple, direct commands with GPT-2 to more nuanced, example-driven prompts with GPT-3. Then came GPT-4, which could handle more complex reasoning & follow more intricate instructions. The evolution is clear: as the models get smarter, our prompts have to get smarter too.

So, how are we going to have to prompt a model that's leaps & bounds ahead of what we have now? It's not about just asking a question anymore. It’s about being a guide, a collaborator, & sometimes, a philosopher. Let's dive into what that future looks like.

The Evolution of Prompting: A Quick Trip Down Memory Lane

To understand where we're going, we need to see where we've been. It’s a pretty wild ride.

The "Just Tell It What to Do" Era (GPT-2)

Remember the early days? With models like GPT-2, prompting was a shot in the dark. You'd give it a starting sentence & hope for the best. The results were often… creative, but not always coherent or useful. It was like talking to a very imaginative but easily distracted toddler. The concept of "prompt engineering" was barely a whisper. We were mostly just experimenting, seeing what crazy stuff the AI would come up with.

The "Show and Tell" Era (GPT-3)

Then came GPT-3, & everything changed. With 175 billion parameters, it was a MONSTER of a model. This is when prompt engineering really started to become a thing. We discovered techniques like:

Zero-Shot Prompting: Just asking the model to do something without any examples. It worked for simple tasks, but it was a bit of a gamble. For instance, "Translate 'Hello, world' to French."
Few-Shot Prompting: This was the real game-changer. By giving the model a few examples of what you wanted, you could guide it to produce much more accurate & specific results. It’s like saying, "Here are a couple of examples of what I mean, now you try." This is when we started to see AI that could perform specialized tasks without needing to be completely retrained.

This was also the era where businesses started to see the real potential. Suddenly, you could build tools that could write copy, answer customer questions, & even generate code. It was also when platforms like Arsturn began to emerge, recognizing the need for businesses to harness this power. Being able to train an AI on your own data & build a custom chatbot that could handle customer inquiries 24/7 was a massive leap forward.

The "Let's Think This Through" Era (GPT-4)

With GPT-4, the models got even more sophisticated. It wasn't just about showing examples anymore; it was about guiding the model's reasoning process. This is where we saw the rise of more advanced techniques:

Chain-of-Thought (CoT) Prompting: This is probably one of the most significant advances. By simply adding the phrase "think step-by-step" to your prompt, you could encourage the model to break down complex problems & reason through them logically. This dramatically improved performance on tasks involving math, logic, & complex reasoning.
Self-Consistency: This takes CoT a step further. Instead of just one line of reasoning, you ask the model to come up with several different ways to solve a problem & then choose the most consistent answer. It’s like getting a second, third, & fourth opinion all at once.

GPT-4 also introduced us to the world of multimodal AI, with the ability to understand both text & images. Suddenly, you could upload a picture of what's in your fridge & ask for a recipe. The possibilities exploded. This is where the idea of an AI assistant started to feel REALLY real.

Prompting in the GPT-5 Era: What to Expect & How to Prepare

So, what's next? If the trend continues, GPT-5 will be more than just an incremental improvement. It will likely have a much deeper understanding of context, nuance, & the world itself. It might be able to handle tasks that are currently far beyond our reach. Here's what that means for how we'll need to prompt it.

1. From Chain-of-Thought to Tree-of-Thought (ToT)

Chain-of-Thought is great, but it's linear. It follows a single path of reasoning. The future is about exploring MULTIPLE paths. Tree-of-Thought (ToT) is a technique that allows the model to explore different branches of reasoning simultaneously.

Think of it like this: when you're solving a tough problem, you don't just follow one line of thought until you hit a dead end. You consider multiple possibilities, weigh the pros & cons of each, & then decide which path to follow. ToT prompting guides the AI to do the same.

How it works:

Decomposition: You break the problem down into smaller steps.
Thought Generation: At each step, you ask the model to generate multiple different "thoughts" or potential next steps.
Evaluation & Pruning: The model then evaluates these different thoughts, discards the ones that seem less promising, & pursues the ones that are most likely to lead to a solution.

This is going to be HUGE for complex problem-solving, strategic planning, & creative brainstorming. You're no longer just getting an answer; you're getting a fully explored decision tree.

2. The Rise of Agentic AI: ReAct & Beyond

The next generation of models won't just be thinkers; they'll be doers. The ReAct framework (Reasoning + Acting) is a perfect example of this. It allows the model to not only reason about a task but also take actions to gather more information.

For example, if you ask a ReAct-powered AI a question it doesn't know the answer to, it won't just hallucinate an answer. It will recognize the gap in its knowledge, formulate a search query, access an external tool (like a search engine or an API), find the information it needs, & then use that information to construct its final answer.

What this means for prompting:

Your prompts will need to become more like high-level directives for an agent. Instead of asking, "What was the weather in Paris yesterday?", you might say, "Plan a weekend trip to Paris for next month, considering my budget of $1000 & my interest in art museums. Book the flights & accommodations, & create a detailed itinerary."

The AI would then break this down into a series of reasoning steps & actions:

Reason: I need to find flights to Paris.
Act: Search for round-trip flights to Paris for next month.
Reason: I need to find hotels within the budget.
Act: Search for hotels in Paris near major art museums with a price under $200/night.
Reason: I need to create an itinerary.
Act: Find the opening hours & ticket prices for the Louvre & Musée d'Orsay.

This is where conversational AI platforms for business will become absolutely essential. Imagine a customer on your website wanting to troubleshoot a complex issue. Instead of just getting a canned response from a simple chatbot, they could interact with an AI powered by a ReAct-like framework. This AI could understand the customer's problem, access your company's knowledge base, look up their order history, & guide them through a step-by-step solution, all in real-time. That’s the kind of next-level customer experience that Arsturn helps businesses build – an AI chatbot trained on your data that doesn't just answer questions but actively helps solve problems.

3. Multimodality is the New Standard

GPT-4 gave us a taste of multimodality, but future models will likely be fully fluent in text, images, audio, & even video. Prompting will no longer be limited to just words. You'll be able to combine different types of input to create much richer & more context-aware requests.

Examples of multimodal prompting:

Show, don't just tell: Instead of describing a piece of furniture you want to build, you could show the AI a sketch, a picture of the wood you're using, & a video of the space it needs to fit into, all while giving it text instructions.
Creative collaboration: You could hum a melody, show the AI a painting for inspiration, & ask it to compose a soundtrack that captures the mood of the art.
Real-world problem solving: Imagine a field technician pointing their phone at a piece of broken machinery. The AI could analyze the video feed, listen to the sound the machine is making, & pull up the relevant repair manual, highlighting the exact steps to fix it.

This is going to revolutionize so many industries. For businesses, this means creating more engaging & helpful customer interactions. A customer could upload a picture of a damaged product, & an AI chatbot from Arsturn could instantly identify the product, understand the issue, & start the return or replacement process. It’s about building meaningful connections through truly personalized & intelligent conversations.

4. The Art of Contextual & Role-Playing Prompts

As models become more advanced, providing the right context will be EVERYTHING. This goes beyond just giving a few examples. It means setting the stage, defining a persona for the AI, & providing a deep well of background information.

Techniques to master:

Role-Playing/Instruction-Based Prompting: This is already powerful, but it will become even more crucial. Instead of just asking for a blog post, you'd say, "You are a seasoned financial advisor with 20 years of experience in retirement planning. Write a blog post for a 30-something audience explaining the benefits of starting a Roth IRA early. Adopt a reassuring & knowledgeable tone."
Contextual Prompting: This involves providing the AI with a large amount of relevant text or data within the prompt itself. With larger context windows (GPT-4 Turbo already has a 128k context window!), you'll be able to feed the model entire books, research papers, or transcripts of meetings to ensure its output is deeply informed.

This is where the power of having an AI trained on your own data really shines. When a business uses a platform like Arsturn to build a no-code AI chatbot, they are essentially providing it with the ultimate context. The chatbot has access to all the company's documents, product information, & support articles, allowing it to provide instant, accurate, & contextually relevant support to every website visitor.

Putting It All Together: A Hypothetical GPT-5 Prompt

So, what would a prompt for a future model look like? Let's imagine we're a marketing team trying to launch a new eco-friendly sneaker.

A GPT-4 Style Prompt: "Write a marketing campaign proposal for a new sneaker called 'TerraStride'. It's made from recycled materials. The target audience is millennials who care about sustainability. Include ideas for social media posts, a blog post, & an email newsletter."

A Hypothetical GPT-5 Style Prompt: "[Persona] You are 'BrandBot', our lead marketing strategist AI. You have access to our company's entire marketing history, brand guidelines (see attached document A), & our latest market research report on sustainable fashion (see attached document B). You also have access to real-time social media trend data.

[Task] Develop a comprehensive, multi-phase launch strategy for our new sneaker, 'TerraStride'.

[Context] TerraStride is our most eco-friendly product to date, made from 100% recycled ocean plastic & a new algae-based foam for the sole. Our primary target is Gen Z & young millennials in urban areas. Our budget for this launch is $250,000.

[Instructions using ToT & ReAct]

Phase 1 (Pre-Launch Teaser):
- Explore three different creative concepts for a teaser campaign. For each concept, generate a mood board (visual inspiration), a short video script (15 seconds), & 5 sample Instagram captions.
- Evaluate each concept based on its potential for viral sharing & alignment with our brand values.
- Select the most promising concept & flesh it out into a detailed 2-week plan.
Phase 2 (Launch Week):
- Identify & vet 10 potential influencers in the sustainability & fashion space who align with our brand. (Action: Access influencer databases & social media APIs).
- Draft a personalized outreach email for each influencer, referencing their recent work.
- Design a multi-platform content calendar for launch week, including high-resolution product images (generate some options based on our product photos - see attached C), behind-the-scenes video clips, & a long-form blog post about the technology behind the algae foam.
Phase 3 (Post-Launch Engagement):
- Outline a customer engagement strategy that leverages user-generated content.
- Propose a structure for a 24/7 customer support chatbot (using Arsturn's platform) that can answer detailed questions about the materials, sizing, & our sustainability mission. The chatbot should also be able to handle returns & exchanges seamlessly.
- Monitor social media mentions in real-time & suggest responses to both positive & negative feedback."

See the difference? It's not a request; it's a strategic partnership. You're providing deep context, defining a role, & guiding the AI through a complex, multi-step reasoning & action process.

Final Thoughts

Look, the way we talk to AI is evolving as fast as the AI itself. The days of simple, one-line prompts are numbered. The future is about collaboration, context, & complex reasoning. It's about treating the AI less like a tool & more like a hyper-intelligent, infinitely capable team member.

Getting ready for models like GPT-5 means we need to start thinking more like architects & less like construction workers. We need to design the problem space, provide the right materials (data & context), & guide the AI's reasoning process. It's a new skill set, for sure, but it's one that will unlock a level of creativity & productivity we can only just begin to imagine.

Hope this was helpful & gives you a glimpse of what's to come. It’s going to be a wild ride, so start practicing your advanced prompting now! Let me know what you think.