So, You're Tired of GPT-5's Lies? Here's How We Can Actually Fix It
Z
Zack Saadioui
8/12/2025
So, You're Tired of GPT-5's Lies? Here's How We Can Actually Fix It
Alright, let's talk about the elephant in the room. GPT-5 is here, & while it's supposed to be the next big thing, a lot of you are probably feeling... underwhelmed. I've been digging through forums, articles, & user complaints, & a common theme keeps popping up: hallucinations. It seems like GPT-5, despite the hype, is still making things up, & in some cases, it's even worse than its predecessors.
I've seen the Reddit threads. I've read the blog posts. Users are saying GPT-5 is a "mess," that it "struggles to follow instructions," & that it "hallucinates more frequently than earlier version and will gaslit you." Some are even mourning the loss of GPT-4o's "charm," saying the new model feels like a "bland corporate memo." Honestly, it's a bit of a letdown.
OpenAI, for their part, claims that GPT-5 has reduced hallucinations, with incorrect claims dropping from 12.9% in GPT-4o to 9.6% in GPT-5. But then, in a demo, it confidently explained how planes work using a common misconception. So, what's the real story?
The truth is, it's complicated. The hallucination problem is a tough nut to crack, & it's not just a GPT-5 issue. It's an inherent challenge with all large language models (LLMs). But here's the good news: we're getting better at understanding why it happens & what we can do to fix it. So, let's dive deep into what's going on under the hood & explore the real solutions that are being developed to make these models more truthful.
Why Do LLMs Lie? The Root of the Hallucination Problem
First off, it's important to understand that LLMs don't "lie" in the human sense. They're not trying to deceive you. They're just very, VERY good at predicting the next word in a sequence. Their whole world is the data they were trained on, & they generate responses based on the statistical patterns in that data. The problem is, that data isn't always perfect. Here are the main culprits behind hallucinations:
Garbage In, Garbage Out: The quality of the training data is EVERYTHING. If an LLM is trained on inaccurate, biased, or outdated information from the internet, it will spit that same garbage back out. Think about all the misinformation on social media – that's a potential training ground for these models.
The Model's Own Limitations: LLMs have their own architectural quirks that can lead to hallucinations. For example, they have a limited "working memory," known as the context window. If a conversation gets too long, older information can get pushed out, leading to what's called "factual drift." The attention mechanism, which is supposed to help the model focus on relevant information, can also get "diluted" in long sequences, causing it to generate made-up content.
The Probabilistic Nature of LLMs: At their core, LLMs are probabilistic models. They're not retrieving information from a database; they're generating it based on what they think is the most likely next word. This is why their responses can sometimes seem plausible but be completely fabricated.
Lack of Real-World Understanding: This is a big one. LLMs don't actually understand the world like we do. They can't reason about cause & effect or apply common sense. They're just really good at mimicking human language. This is why they can fail at tasks that require even basic logic.
The Big Guns: How We Can Actually Fight Hallucinations
So, now that we know why hallucinations happen, what can we do about it? Luckily, a lot of smart people are working on this problem, & there are a bunch of promising techniques being developed. Here's a rundown of the most effective strategies:
1. Retrieval-Augmented Generation (RAG): The Fact-Checker on Steroids
This is probably the most powerful tool we have right now to combat hallucinations. RAG is a technique that connects the LLM to an external, authoritative knowledge source. Instead of just relying on its training data, the model can "look up" information in real-time to make sure its answers are accurate.
Think of it like this: instead of asking a student to answer a history question from memory, you give them a textbook to consult. That's what RAG does for LLMs. This is why GPT-5's hallucination rates are so much lower when it has web access.
This is where a tool like Arsturn comes in. Arsturn helps businesses create custom AI chatbots trained on their own data. This is a form of RAG. Instead of relying on the general knowledge of a massive LLM, a business can create a chatbot that's an expert on its own products, services, & policies. This dramatically reduces the chances of the chatbot hallucinating & giving customers incorrect information. It's a game-changer for customer service, where accuracy is paramount.
2. Prompt Engineering: Giving the LLM Better Instructions
Sometimes, the simplest solution is the most effective. The way you phrase your prompt can have a huge impact on the quality of the response. Here are a few prompt engineering techniques that can help reduce hallucinations:
One-shot & Few-shot Prompts: This involves giving the model an example of what you want. For example, if you want a summary in a specific format, you can provide an example of a summary in that format in your prompt. This "nudges" the model in the right direction.
Chain-of-Thought (CoT) Prompting: This is a really cool technique for improving the model's reasoning abilities. You ask the model to "think step-by-step" before giving its final answer. This forces it to break down complex problems into smaller, more manageable steps, which can lead to more accurate results.
3. Fine-Tuning & Reinforcement Learning: Training for Truthfulness
Fine-tuning is the process of taking a pre-trained LLM & training it further on a smaller, more specific dataset. This can help the model become an expert in a particular domain, which can reduce hallucinations.
Reinforcement Learning from Human Feedback (RLHF) is a specific type of fine-tuning where humans provide feedback on the model's responses. This helps the model learn what humans consider to be a "good" response, which can include things like factuality & helpfulness.
4. Guardrails & Verification Layers: The Safety Net
Another approach is to build "guardrails" around the LLM. This can involve having a second LLM or a human-in-the-loop verify the first model's output. You can also add "fact confidence tags" to the model's responses, so users know how confident the model is in its answer. This transparency can help build trust, even when hallucinations can't be completely eliminated.
The Future of Truthful AI
The reality is, we're probably never going to eliminate hallucinations completely. It's an inherent part of how these models work. But that doesn't mean we can't make them a whole lot better. The combination of techniques I've talked about – from RAG & prompt engineering to fine-tuning & guardrails – is already making a big difference.
For businesses, the key is to take control of the AI's knowledge base. You can't just rely on a general-purpose model to be an expert on your business. That's why building a no-code AI chatbot with a platform like Arsturn is so powerful. By training a chatbot on your own data, you can create a conversational AI that provides accurate, reliable information to your customers, boosting conversions & providing personalized experiences.
At the end of the day, the goal is not to create a perfect, all-knowing AI. It's to create AI that is a helpful, trustworthy assistant. & with the right tools & techniques, we're getting closer to that goal every day.
So, what do you think? Have you been frustrated with GPT-5's hallucinations? Have you found any tricks that help? Let me know in the comments. I'd love to hear your thoughts. Hope this was helpful