Control GPT-5 Verbosity & Relevance

8/11/2025

Taming the Beast: How to Handle GPT-5's Verbosity & Off-Topic Answers

Alright, let's talk about something anyone who's used a large language model (LLM) knows all too well: the rambling. You ask a simple question & you get a five-paragraph essay complete with an introduction, three supporting points, & a conclusion. Or, you ask for something specific & the AI wanders off into a completely unrelated topic. It's like talking to that one friend who's brilliant but can't for the life of them just get to the point.

With every new iteration of GPT, the models get more powerful, more knowledgeable, & frankly, more capable of generating HUGE amounts of text. While that's amazing, it can also be a massive headache. For developers, businesses, & even casual users, this verbosity & tendency to go off-topic can be a major roadblock. You're left trying to sift through the noise to find the signal.

But here's the good news: with the release of GPT-5, it looks like OpenAI has been listening. REALLY listening. They've rolled out some new tools & features that are specifically designed to tackle these exact problems. It's a pretty big deal. We're moving from a place of simply wrangling the beast with clever prompts to having actual dials & knobs we can turn.

So, how do we tame this incredibly powerful new beast? It turns out it's a combination of understanding the new built-in controls & still applying some of that good old-fashioned prompt engineering. Let's dive in.

The Age-Old Problem: Why Do LLMs Love to Talk So Much?

Before we get into the solutions, it helps to understand why these models tend to be so chatty & sometimes lose focus. It's not because they're trying to be annoying, I promise.

At their core, LLMs are prediction engines. They are trained on a truly mind-boggling amount of text from the internet – everything from scientific papers & news articles to blog comments & social media posts. Their fundamental job is to predict the next most likely word in a sequence. This training process has a few side effects:

The "Good Student" Syndrome: The model has learned that a thorough, well-structured answer is often rewarded in its training data. Think about it: encyclopedia entries, detailed articles, & comprehensive guides are considered high-quality content. The AI mimics this, aiming to provide what it thinks is a complete & helpful response, which often translates to a verbose one.
Hedging Its Bets: Sometimes, when a model isn't 100% certain about the user's intent, it will provide a broader answer that covers multiple possibilities. It's trying to be helpful by casting a wide net, but this can easily come across as an off-topic detour.
Lack of True Understanding: This is the big one. The AI doesn't understand context in the same way a human does. It doesn't have personal experiences or a world model to ground its "thoughts." It's working with statistical relationships between words. This can lead it down strange paths if a keyword in your prompt has a strong association with an unrelated topic in its training data.

For anyone trying to use these models in a professional setting, this is more than just a minor annoyance. For a developer building an application, an overly verbose response can break a JSON format or overload a user interface. For a business using an AI for customer support, an off-topic answer can frustrate a user & lead to a poor customer experience. It's a real problem.

The Game Changer: GPT-5's New Control Knobs

This is where things get exciting. With previous models, our main tool for controlling output was the prompt itself. We had to get creative with instructions like "Be concise," "Answer in one sentence," or "Stick to the topic of X." It worked, but it was an imperfect art.

GPT-5 introduces new API parameters that give us direct, explicit control over the model's output. This is a HUGE leap forward. Two of the most important new additions are the

verbosity

parameter & the

reasoning_effort

setting.

The "Verbosity" Parameter: Your New Best Friend

This is exactly what it sounds like. The

verbosity

parameter lets you give the model a hint about how expansive you want its reply to be, without having to rewrite your entire prompt. This is HUGE. You can keep your core instructions the same & just toggle the level of detail you need.

According to the documentation, it seems to have a few levels:

Low Verbosity: This is for when you need a terse, to-the-point answer. Think minimal prose, just the facts. This is perfect for things like data extraction, getting a quick definition, or generating a functional piece of code without all the explanatory comments.
Medium Verbosity (The Default): This gives you a balanced level of detail. It's probably what most people will use for general-purpose chat & content creation. It's helpful without being overwhelming.
High Verbosity: This is for when you want the firehose. It's great for audits, teaching, or getting a comprehensive, production-ready script with all the bells & whistles like argument parsing, best-practice tips, & detailed explanations.

Let's imagine a practical example. You ask GPT-5 to write a Python script to sort a million random numbers.

At low verbosity, you might just get the raw, functional code. Clean, simple, does the job.
At medium verbosity, you'd likely get the code with some explanatory comments & maybe wrapped in a function.
At high verbosity, you'd get a full-blown script with detailed comments, error handling, multiple sorting methods for comparison, performance timing, & usage notes.

The ability to control this with a single parameter is incredibly powerful. It means you can use the same base prompt for different audiences or different stages of a workflow.

"Reasoning Effort" & The Auto-Switcher: Smarter Than Your Average AI

Another major innovation in GPT-5 is how it decides how to think. In the past, you'd get one mode of operation. Now, GPT-5 has a system that can switch between different approaches based on your query.

Inside ChatGPT, this works through a new "real-time router" or "auto-switcher." You just talk to it, & the system decides whether it needs a quick, almost instant reply or if it needs to engage in a more deliberate, multi-step reasoning process. This is designed to make the user experience much simpler. You don't have to choose a model; GPT-5 figures out what's needed on the fly.

For developers using the API, this is exposed through a parameter called

reasoning_effort

. You can set this to "minimal" for tasks that are deterministic & lightweight, like formatting text, simple classification, or extracting specific information. This minimizes latency & gets you a response as fast as possible. For more complex tasks that require planning or multi-step analysis, you'd let the model use its full reasoning power.

This directly helps with off-topic answers. When a model is forced to rush through a complex problem, it's more likely to make a mistake or get sidetracked. By giving it the appropriate "thinking time," it can stay on track & provide a more coherent & relevant response. In fact, OpenAI claims that GPT-5 in "thinking" mode produces FAR fewer hallucinations & factual errors than previous models. The numbers are pretty staggering: responses are reportedly 45% less likely to have factual errors than GPT-4o with web search, & a whopping 80% less likely than the o3 reasoning model.

The Business Impact: Why This Matters for Real-World Applications

These new controls aren't just cool tech features; they have a direct impact on how businesses can use AI effectively. This is especially true in areas like customer engagement & support.

Honestly, one of the biggest challenges of deploying an AI chatbot for customer service has been its unpredictability. A customer asks, "Where is my order?" & you want the chatbot to provide the tracking number & a link, not a philosophical discussion on the history of logistics. Verbosity & off-topic answers create friction & frustration, which is the exact OPPOSITE of what you want from customer service.

This is where a platform like Arsturn comes into the picture. Arsturn helps businesses build no-code AI chatbots that are trained specifically on their own data. Think of it as giving the AI a very specific, curated library to study from. Instead of knowing everything about everything, it knows everything about your products, your policies, & your FAQs. This is the first, most crucial step in keeping an AI on-topic.

Now, layer in the new capabilities of GPT-5. With Arsturn, you could build a customer service bot trained on your company's knowledge base. Then, using the new API controls, you could set the

verbosity

to "low" for most standard queries. When a customer asks "What are your business hours?", they get a direct, simple answer, not a friendly paragraph about the importance of work-life balance.

But what if the customer has a more complex problem? "My product arrived damaged & I need to process a return & get a replacement for an international order." This is where the

reasoning_effort

comes in. The system could recognize the complexity & allow the model to use its deeper reasoning abilities to walk the user through the multi-step process, access different pieces of information, & provide a comprehensive solution.

By combining a focused knowledge base from Arsturn with the fine-grained output controls of GPT-5, businesses can create AI assistants that are not only knowledgeable but also efficient & respectful of the user's time. They provide instant, accurate support 24/7, answer questions precisely, & engage with website visitors in a way that feels helpful, not distracting. This is how you move from a novelty chatbot to a truly effective business tool that can boost conversions & build meaningful connections with your audience.

Don't Throw the Baby Out with the Bathwater: Prompt Engineering Still Matters

While the new parameters are fantastic, they don't make prompt engineering obsolete. Think of it like this: the

verbosity

reasoning_effort

parameters are the settings on your camera, but prompt engineering is how you frame the shot. You need both to get a great picture.

Here are some classic & updated prompting techniques to use with GPT-5 to keep it perfectly on track:

1. The Power of Persona & Role-Playing

This is still one of the most effective techniques. Tell the AI who it is.

Old way: "You are a helpful assistant." (Too generic)
New way: "You are a senior technical support agent for a SaaS company. Your responses must be clear, concise, & focused only on solving the user's technical issue. Do not engage in small talk. Use a professional but direct tone."

The more specific the persona, the better the guardrails for the AI's behavior.

2. Clear Constraints & "Negative" Instructions

Be explicit about what you don't want.

Old way: "Summarize this article."
New way: "Summarize the key findings of this article in three bullet points. Do not include an introduction or a conclusion. Do not mention any information not present in the article."

Telling the AI what to avoid is just as important as telling it what to do. This helps prevent it from "helpfully" adding extra information that you don't want.

3. The "Chain of Thought" & "Show Your Work" Method

For complex problems where you're worried about the AI jumping to a wrong conclusion, ask it to explain its reasoning process. This is especially useful when you're not using the maximum

reasoning_effort

Prompt: "I need to calculate the total cost of a project. The variables are X, Y, & Z. First, explain the formula you will use. Second, calculate each part of the formula step-by-step. Finally, provide the total cost."

This forces the model to slow down & externalize its "thought" process, which often leads to more accurate & on-topic results. GPT-5 is designed to be better at these long-running tasks involving multiple steps, so this technique should be even more effective now.

4. The Art of the Example (Few-Shot Prompting)

Give the AI an example of the output you want. This is incredibly powerful for formatting.

Prompt: "Extract the name, company, & job title from the following text. Format it as JSON. Here is an example: Text: 'Jane Doe is the CEO of Acme Inc.' JSON:
1{'name': 'Jane Doe', 'company': 'Acme Inc.', 'job_title': 'CEO'}
Now, process this text: 'John Smith works as a Lead Engineer at Genericorp.'"

By providing a perfect example, you're giving the model a crystal-clear template to follow. This is far more effective than just describing the format you want.

5. Structuring Your Prompt for Success

Don't just write a blob of text. Use clear headings or delimiters to structure your prompt.

Prompt:
1### INSTRUCTIONS ###

1Analyze the customer feedback below.

1Identify the core sentiment (Positive, Negative, Neutral).

1Extract the key product feature being discussed.

1Output the result as a JSON object.
1### CUSTOMER FEEDBACK ###

1"I absolutely love the new dashboard design, but the mobile app keeps crashing whenever I try to upload a file."
1### OUTPUT ###

This structure makes it incredibly easy for the model to understand the different parts of your request & what it needs to do.

Putting It All Together: A Practical Workflow

So, what does this look like in practice? Let's say you're building a feature for your app that uses AI to help users write marketing copy.

Define the Goal: You want the AI to generate a short, punchy headline for a social media post. Verbosity is the enemy here.
Choose Your Model & Settings: You'd likely use one of the faster, cheaper models like
1gpt-5-mini
via the API. You would set
1verbosity: "low"
&
1reasoning_effort: "minimal"
because this is a creative but not deeply complex task.
Craft the Prompt: You'd use a combination of techniques.
- Persona: "You are an expert social media copywriter specializing in viral hooks."
- Constraints: "Your task is to generate 5 headlines for a product launch. The product is a new smart coffee mug that keeps drinks at a perfect temperature for 3 hours. Each headline must be under 10 words. Do not use hashtags. Do not ask any questions."
- Example: "Here's an example of a good headline: 'Never sip cold coffee again.' Now, generate 5 more for the smart coffee mug."

By combining the new GPT-5 parameters with smart prompting, you've created a highly reliable & controlled system. You've minimized the risk of getting a long, rambling response or a headline about the history of coffee cultivation in South America. You get exactly what you need, quickly & efficiently.

This is the future of working with AI. It's less about wrestling with a black box & more about being a skilled operator with a sophisticated set of tools. GPT-5 has handed us the user manual & the control panel. Now it's up to us to learn how to use them.

Hope this was helpful & gives you a good starting point for taming the beast! It's a pretty exciting time to be building with this tech. Let me know what you think.