Unlock GPT-5's Full Power: Advanced Prompting Techniques

8/12/2025

So, you've gotten your hands on GPT-5, & it's pretty incredible. But you've probably noticed something a little quirky. You ask it a simple question, & the response, while fast, feels... a bit lightweight. It's almost like it's not using its full brainpower. & honestly, you're not wrong.

Turns out, there's a fascinating reason for this, & it all comes down to a super-smart system design that's meant to be more efficient. But what if you WANT the full, deep-thinking expert for every query? Well, you're in the right place. We're going to dive deep into why GPT-5 does this &—more importantly—how to "force" it to use its most powerful internal models, even for the simple stuff.

The Big Secret: It's Not One Model, It's a Team of Experts

Here’s the thing that’s not immediately obvious: GPT-5 isn't a single, giant AI model. Think of it more like a team of specialists. This is a concept in AI called a "Mixture of Experts" (MoE). Instead of one massive, monolithic network that handles every single request, an MoE architecture uses multiple smaller, specialized "expert" networks.

When you send a prompt, there's a component called a "router" or "gating network" that takes a quick look at your question & decides which expert (or combination of experts) is best suited to answer it. This is the core of how GPT-5 operates. OpenAI has confirmed that GPT-5 is a unified system with a smart, efficient model for most questions, & a deeper "thinking" model for harder problems. A real-time router decides which one to use.

This is actually a HUGE leap forward. It allows the system to be incredibly efficient. Why fire up a supercomputer-level brain to answer "what's the capital of France?" A smaller, faster model can handle that in a fraction of the time & with a fraction of the computational cost. This dynamic intelligence scaling is a game-changer.

The system is even designed to learn & improve in production. It looks at signals like whether you give a thumbs up or down, edit the response, or retry the prompt to get better at routing future requests.

So, What's Under the Hood of GPT-5?

The GPT-5 ecosystem is made up of a few different players, all orchestrated by this router. You've got:

A fast, "smart" model: This is your go-to for routine, low-latency prompts. It's quick, efficient, & gets the job done for the majority of everyday questions.
A "thinking" model (GPT-5 thinking): This is the heavyweight. It's designed for complex reasoning, nuanced tasks, & deep problem-solving. This is the one you want for debugging code, analyzing complex data, or writing a detailed report.
Other variants like Mini & Nano: Azure's documentation on their AI Foundry, which uses GPT-5, mentions "GPT-5 mini" for real-time experiences & "GPT-5 nano" for ultra-low-latency, high-volume requests. These are even smaller, more specialized models for specific, less complex tasks.

You don't have to manually pick these (though Pro users have some options to select them). The router is supposed to do it for you seamlessly. It looks at the complexity of your prompt, the context of the conversation, & even your intent to decide which model gets the call.

How to Nudge the Router: Forcing the "Thinking" Model

Okay, so we know why it's happening. But what if you're working on something that seems simple on the surface but you want the full power of GPT-5's reasoning? Maybe you're brainstorming creative ideas from a simple seed, or you want a deeply philosophical take on a straightforward question.

Here's how you can "trick" the router into calling up the big guns. It's all about how you frame your prompt.

1. Explicitly Ask for Deeper Thinking

This is the most direct approach & surprisingly effective. OpenAI has stated that the router responds to your explicit intent. So, tell it what you want!

Instead of: "Explain photosynthesis."
Try: "Think hard about this: Explain photosynthesis from first principles, as if you were teaching it to a college-level biology student. Break down the chemical reactions step-by-step."

Phrases like "think hard about this," "give me a detailed, expert-level explanation," or "analyze this from multiple perspectives" can signal to the router that this isn't a simple request, even if the core topic is common.

2. Chain of Thought (CoT) Prompting

This is a POWERFUL technique. Instead of asking for the final answer, you ask the model to show its work. Breaking a problem down into smaller, logical steps forces it to engage in a more complex reasoning process.

Instead of: "What's a good marketing strategy for a new coffee shop?"
Try: "I'm developing a marketing strategy for a new coffee shop. First, identify the target audience. Second, suggest three unique selling propositions. Third, outline a 3-month marketing plan across social media, local SEO, & community events. Please think step-by-step."

By structuring your prompt this way, you're essentially creating a mini-task list that requires more than a surface-level answer.

3. Few-Shot Prompting

Few-shot prompting is where you give the model a few examples of the kind of output you want. This not only helps with formatting but also signals the quality & depth you're looking for.

Example: "Here are a few examples of simple questions with deep, philosophical answers. Q: What is a chair? A: A chair is more than an object for sitting; it is a manifestation of human ingenuity, a silent testament to our need for rest & contemplation. It represents a temporary abdication of movement, a tool for social gathering, & an element of design that shapes our physical & social environments. Q: What time is it? A: To ask for the time is to acknowledge our place in a linear progression we've imposed on the universe. It's a human construct, a shared agreement to measure the unmeasurable, providing structure to our lives while reminding us of their fleeting nature. Now, using this style, answer the following question: What is a window?"

This technique basically forces the model to mimic the complexity of your examples, likely kicking it up to the "thinking" model.

4. Add Complexity & Constraints

Give the model more to work with. Add constraints, specify a persona, or ask for a specific format. The more complex the request, the more likely the router is to send it to the more powerful model.

Instead of: "Write a poem about the ocean."
Try: "Write a five-stanza poem about the ocean from the perspective of an old lighthouse keeper who has seen its calm & its fury. The poem must be in iambic pentameter & include at least three literary devices, such as a metaphor, personification, & a simile."

This level of detail requires a much more sophisticated level of language generation & understanding.

5. Generate Knowledge First

This is an advanced technique where you ask the model to generate some background information before tackling your main question. This warms up its reasoning capabilities.

Instead of: "Should my business invest in AI?"
Try: "Before you answer my main question, first generate a brief overview of the current state of AI in the e-commerce industry, including key trends & common applications. Once you've done that, use that knowledge to argue for & against my small e-commerce business investing in AI, & then provide a final recommendation."

6. Tree of Thoughts (ToT) Prompting

For REALLY complex problems, you can use a technique like Tree of Thoughts. This involves asking the model to explore multiple reasoning paths.

Example: "I need to decide whether to launch a new app. Imagine three different experts are considering this problem: a financial analyst, a marketing guru, & a software developer. Have each expert write down one step of their thinking process. Then, have them share their thoughts & proceed to the next step. If an expert realizes their path is a dead end, they should state why & drop out. Continue this process until a final, well-reasoned decision is made."

This is probably overkill for most tasks, but it's a great example of how to force the model into its most deliberative, analytical state.

How Arsturn Can Leverage This for Your Business

Understanding these nuances is cool for personal use, but it's CRUCIAL for businesses. When you're building customer-facing tools, you need to control the experience. This is where a platform like Arsturn comes in.

When you're dealing with customer service, for instance, not all questions are created equal. "Where's my order?" is a simple, factual query. A smaller, faster model is perfect for that. It's efficient & provides an instant answer. But what about a more complex question like, "Which of your camera models is best for shooting action sports in low light, & what are the trade-offs between them?" That requires deeper reasoning.

With Arsturn, you can build no-code AI chatbots trained on your own data. This allows you to design conversational flows that can handle both types of queries effectively. For simple, frequent questions, the chatbot can provide instant, accurate answers 24/7. For more complex, sales-oriented, or technical support questions, you can design prompts within your Arsturn chatbot that use these advanced techniques to elicit more detailed, thoughtful responses, helping to guide customers & boost conversions. It's about using the right level of intelligence for the right task to create the best possible customer experience.

Tying It All Together

So, there you have it. GPT-5's tendency to use smaller models for simple questions isn't a flaw; it's a feature. It's a sophisticated system designed for efficiency. But the power is still in your hands. By being more deliberate & creative with your prompting, you can influence the system & call upon its most powerful reasoning abilities whenever you need them.

It's a bit like learning to drive a car with different performance modes. Sometimes, "eco" mode is all you need. But when you want to feel the full power of the engine, you just need to know which buttons to press.

Hope this was helpful! Let me know what you think, & if you've found any other cool ways to get GPT-5 to really flex its muscles.