Your Custom GPTs Aren't Broken: Here's How to Fix Them
Z
Zack Saadioui
8/12/2025
Hold On, Your Custom GPTs Aren't Broken (But You're Right to Be Annoyed)
Let's be honest, the last few weeks have been a rollercoaster for anyone who relies on ChatGPT for serious work. You woke up one day, and your finely tuned custom GPTs—the ones you’d spent months perfecting—suddenly felt… dumb. Right? You're not imagining it.
The internet is full of people saying that "GPT-5" is a massive step backward. I've seen the threads: "It doesn't listen," "It's making things up," "My old prompts don't work anymore." People are frustrated, and frankly, they have every right to be. It feels like we had this incredible tool, this reliable partner, and overnight it was replaced by a less capable, more stubborn intern.
But here’s the thing, and this is the core of it all: the problem isn't that GPT-5 is "wrong" or "broken." The issue is that what we're calling "GPT-5" in the ChatGPT interface isn't what we think it is. And the "legacy issues" you're running into are totally fixable, once you understand what’s actually happening under the hood.
So, grab a coffee. Let's unpack what's REALLY going on & how to get your custom GPTs back to their former glory.
The Big Misconception: It’s Not One Model, It’s a Traffic Cop
The number one source of all this frustration is a misunderstanding of what OpenAI actually rolled out. We all expected a traditional upgrade: GPT-4 was great, GPT-4o was even better, so GPT-5 should be the next logical step up, right? A single, more powerful model.
Turns out, that's not what happened.
What we're interacting with in the ChatGPT interface is more like a sophisticated routing system. Think of it as a traffic cop. When you send a prompt, this router quickly analyzes it & decides where to send it. Is it a simple question? It gets sent to a faster, lighter model. Is it a complex, multi-step reasoning problem? It gets routed to the heavy-duty "thinking" model. Sometimes, it might even use a few different models in tandem to generate a response.
This is a HUGE architectural shift. Before, we were always talking to a single, predictable model. Now, we're talking to a system that makes a judgment call on our behalf, often without us even knowing it. This is why everything suddenly feels so inconsistent. The "personality" of your GPT seems to have vanished because you might be talking to a different underlying AI with every other prompt.
One user on Reddit put it perfectly: "It's literally just a router switching between existing models." And while that sounds like a criticism, it’s actually the key to understanding & fixing the problem. The frustration isn't about the technology itself being bad; it's about a total mismatch in expectations. We were expecting a new brain, & instead, we got a new nervous system.
This explains so much of the backlash. The shorter, less nuanced responses? That’s likely the router sending your prompt to a speed-optimized chat model instead of the deep-reasoning one you were used to. The broken workflows? They were built for a consistent personality & now they're getting unpredictable responses from a committee of models.
The good news? You can learn to work with this new system. The even better news? For those who need absolute consistency, there are ways to bypass the router.
Why Your Custom GPTs Feel "Legacy" & How to Fix Them
Okay, so we've established the "why." Now for the "how." Let's break down the most common complaints I've seen about custom GPTs post-update & go through some practical, no-nonsense fixes.
Problem 1: The "Dumbing Down" Effect & Loss of Nuance
This is probably the most common complaint. Your custom GPT, which used to provide detailed, insightful, & stylistically perfect answers, now gives you generic, surface-level responses. A translator GPT starts inventing words. A writing assistant loses its voice. A research bot that used to be a goldmine of information now just gives you a summary of the first paragraph of a Wikipedia page.
Why it's happening: The router. Your prompt, which used to be handled by the single, powerful GPT-4o model, is now being judged as "simple" by the new routing system. So, it's being sent to a faster, less "thoughtful" model to save on resources. It's not that the powerful model is gone; it's just that your prompt isn't being granted access to it.
How to fix it:
Be More Explicit in Your Instructions: The single most effective change you can make is to be WAY more demanding in your custom GPT's instructions. You can't assume it knows you want a deep, thoughtful answer anymore. You have to force the router to recognize the complexity of the task.
Old Instruction: "You are a marketing expert who writes great copy."
New, Better Instruction: "You are a world-class marketing strategist. Your primary function is to provide in-depth, multi-faceted analysis. For every prompt, you MUST follow a multi-step reasoning process. First, analyze the user's request. Second, identify the underlying goal. Third, generate three distinct, creative options. Fourth, for each option, provide a detailed rationale citing established marketing principles. Your responses should be a minimum of 500 words & adopt a highly analytical & sophisticated tone."
See the difference? We're not just telling it what to be; we're telling it how to think. This kind of instruction is more likely to be flagged by the router as a complex task, ensuring it gets sent to the more powerful model.
Demand a Specific Format: The router seems to respond well to formatting constraints. If you ask for a specific output structure, it's more likely to engage a model that can handle complex instructions.
Add this to your instructions: "All outputs MUST be formatted in Markdown. Use H2 headings for main sections & H3 headings for sub-sections. Key terms must be bolded. Conclude every response with a 'Key Takeaways' section in a blockquote."
Use "Thinking" Triggers: Some users have found success by adding phrases that explicitly call for reasoning at the beginning of their prompts.
Start your prompts with phrases like: "Engage in a step-by-step reasoning process to answer the following..." or "Adopt the persona of an expert & provide a detailed analysis of..."
Problem 2: My Prompts & Workflows Are Completely Broken
You had a system. A beautiful, elegant system of prompts that worked every single time. Now, they're useless. The output is unpredictable, the formatting is all over the place, & the whole workflow has ground to a halt.
Why it's happening: Again, the router is the culprit. Your workflow was designed for a single, consistent AI. Now, different steps in your workflow might be handled by different models, leading to a total breakdown in continuity.
How to fix it:
Consolidate Instructions into a Single Prompt: Instead of a multi-turn conversation where you build on previous responses, try to pack as much context & instruction into a single, massive prompt as you can. This is less conversational, but it's more likely to be treated as a single, complex task by the router.
Reference Previous Context Explicitly: Don't assume the model remembers the last turn of the conversation with the same fidelity. In your custom GPT's instructions, you can command it to always consider the context.
Add this to your instructions: "In every response, you must reference the key points of the user's previous prompts to ensure continuity. Before generating a new response, briefly summarize your understanding of the conversation so far."
For Businesses: Offload the Workflow to a Dedicated System: Here's where we need to think beyond just prompting. If you have a business workflow that depends on consistent AI responses—like customer support, lead qualification, or internal knowledge management—relying on the public-facing ChatGPT interface is now a risky game.
This is EXACTLY why platforms like Arsturn are becoming so crucial. Instead of fighting with a router you can't control, you can build your own custom AI chatbot on a platform that guarantees consistency. With Arsturn, you train the AI specifically on your data—your help docs, your product info, your website content. The AI isn't guessing; it's using the information you gave it. This means you get predictable, accurate, & on-brand responses EVERY single time. It's the perfect solution for businesses that need to automate customer service or website engagement without the chaos of a public model update. You're not just giving it instructions; you're giving it a brain that you built.
Problem 3: The Safety & "Refusal to Answer" Issue
This one is particularly infuriating for people doing legitimate research. You ask for demographic data for a sociology paper or discuss sensitive but non-malicious topics, & you get a stern lecture about safety guidelines. It feels like the AI has become overly cautious & unhelpful.
Why it's happening: This is a combination of the routing system & OpenAI's evolving safety policies. The router might be sending your query to a model with a stricter set of guardrails. It's a blunt instrument, and it often assumes the worst, shutting down conversations that are perfectly valid.
How to fix it:
Provide Overwhelming Context: This is the most important step. Don't just ask your question. Frame it. Explain why you're asking, what the purpose of the research is, & how you intend to use the information ethically.
Bad Prompt: "Give me the average income by race in New York City."
Good Prompt: "I am working on a sociological research paper for my university course on urban inequality. The paper's goal is to analyze the economic factors contributing to neighborhood segregation. For this academic, non-commercial purpose, please provide the most recent available data on the average household income broken down by race & ethnicity in the five boroughs of New York City. Please cite your sources."
Declare Your Intent: Sometimes, you have to be incredibly direct. Start your prompt by stating your intentions clearly.
"My intent is purely academic & for research purposes. I am not seeking to generate stereotypes or harmful content. With that understanding, please..."
Use the API: This is often the ultimate workaround. The API gives you more direct access to the models without the same level of conversational routing & the sometimes-overzealous "personality" of the ChatGPT interface. You have more control & can often get straight answers to data-driven questions that the chat interface would shy away from.
For Developers & Businesses: The API Is Your Safe Haven
If you're building applications or business processes on top of OpenAI's models, the single most important takeaway from this whole situation is this: move your critical workflows to the API.
The ChatGPT interface is a consumer-facing product. It's subject to change, A/B testing, & architectural shifts like this router system. The API, on the other hand, is built for stability & predictability.
When you use the API, you can specify the exact model you want to use. You're not at the mercy of a router. You can call
1
gpt-5-reasoning
(or whatever the specific model name is) directly & know that you're getting the same powerful model every time. This is how you build reliable, scalable AI features.
For many businesses, however, managing API calls, context windows, & the infrastructure around it can be a headache. This is, once again, where no-code platforms come into play. A solution like Arsturn essentially acts as a user-friendly layer on top of this powerful technology. It allows businesses to harness the consistency of specific AI models without needing a team of developers to manage the backend. You can build a no-code AI chatbot, train it on your own data, & deploy it to your website to generate leads, answer customer questions, & boost conversions. It takes the power of the API & makes it accessible to everyone, giving you the best of both worlds: the cutting-edge AI capabilities without the unpredictability of the consumer chat interface.
It's Not a Downgrade, It's a Different Beast
So, is GPT-5 a failure? No. In many official benchmarks, it's incredibly powerful, showing strong results in math, science, & coding. It can even handle long, multi-step tasks better than its predecessors.
The problem was never the underlying tech. It was the interface. The rollout created a jarring experience because it changed the fundamental way we interact with the AI. It swapped a predictable partner for an unpredictable committee.
But now you know the secret. You know about the router. You know that you need to be more explicit, more demanding, & more structured in your instructions. You know that for real business applications, you need the stability that comes from the API or from platforms built on top of it.
The "legacy issues" you're facing aren't a sign that your custom GPTs are obsolete. They're a sign that you need to adapt your approach. By being more intentional with your prompts & understanding the new architecture, you can not only fix the problems you're having but also unlock even more power from this new generation of AI.
I hope this was helpful. It's been a confusing time for a lot of people, but with a little bit of inside knowledge, you can get back to building amazing things. Let me know what you think—have you tried any of these fixes? What's worked for you?