When AI Makes Stuff Up: A Guide on How to Prevent AI Hallucinations

Ask your AI assistant for a simple fact. It may look you straight in the digital eye and assert — with full confidence — that the capital of France is London. That's an AI hallucination: the model fabricating information. Between 3% and 10% of all Generative AI outputs are complete inventions. 

In high-stakes sectors — such as healthcare, finance, and law — these aren't harmless quirks. A single hallucinated symptom can derail a diagnosis. One erroneous compliance detail can trigger millions in penalties. A made-up precedent can sink an entire legal argument.

So the real question becomes: are you tracking it? And how to prevent AI hallucinations? We've outlined AI hallucination detection methods and how to tame it before it runs off the rails.

Not long ago, the internet exploded with a story straight out of a legal thriller: ChatGPT invented a court case and dragged a lawyer into trouble. U.S. attorney Stephen Schwartz used the chatbot to gather precedents for an aviation lawsuit — and ChatGPT delivered polished, convincing citations. It appeared later several of those cases did not exist. When questioned, the model doubled down, insisting they were real. Schwartz trusted the AI and didn't verify a single reference.

These are striking examples of an AI hallucination: weeks later, the mayor of an Australian city threatened to sue OpenAI after ChatGPT falsely claimed he'd served prison time for bribery. AI-generated falsehoods like these are becoming harder to ignore.

Even Vectara, a startup studying hallucination rates, found major differences across models: GPT-4 and GPT-4 Turbo hallucinated only about 3% of the time in summarization tests, while Google's PALM 2 hit 27%.

According to Duncan Curtis, senior vice president of GenAI and AI Product at Sama, 

This figure is so problematic that a key selling point for a recent update to Anthropic's Claude model was the claim that it was now twice as likely to answer questions correctly. 

While a 2.5% error rate may seem like a small risk in isolation, the numbers quickly become alarming when scaled to the massive user base of popular tools like ChatGPT, which receives an estimated 10 million queries daily. At that volume, a 2.5% hallucination rate translates to 250,000 false outputs every single day, or 1.75 million per week. 

Why does this happen in the first place? Hallucinations aren't random glitches. They stem from the core mechanics of how generative models are built and trained.

LLMs are optimized for predicting the next token, and generating “helpful” answers (RLHF). Neither objective explicitly prioritizes factual accuracy. As a result, the model selects the most plausible explanation, rather than the true one. Furthermore, fluency often wins over correctness.

Training data, even at massive scale, contains noise, inconsistencies, and outdated information. This exposure creates fragile statistical patterns that surface as hallucinations. Familiar sources of issues:

Models tend to apply learned patterns too broadly. Because they cannot explicitly represent uncertainty, they produce confident but incorrect answers. Signals of overgeneralization:

The more formal or regulated the domain (finance, healthcare, law), the more dangerous overgeneralization becomes.

Vague or multi-interpretation questions force the model to fill in details on its own, often leading to hallucinations. Example:

“Tell me about Apple's policies.” → Could refer to privacy, HR, device repair, supply chain ethics, security, or environmental impact.

Without retrieval (RAG) for hallucination, APIs, or specialized tools, the model operates solely on its internal representation of the world. Consequences:

Sometimes the hallucination isn't caused by the model's knowledge but by the format the user demands. Typical triggers:

The more rigid the output requirements, the more likely the model is to “force-fit” information compromising accuracy.

These occur within the model itself, without referencing any external information. The model produces false statements because its internal statistical representations are incomplete, inconsistent, or overly generalized.

Generative AI hallucination examples:

These appear when the model interacts with external data — documents, APIs, search systems — and misinterprets or misrepresents that information.

Generative AI hallucination examples:

Even with accurate external data, the model can still hallucinate during interpretation, extraction, or summarization — especially with complex, noisy, or ambiguous sources.

Some areas are too high-stakes for AI guesses. Always get an expert's eyes on these:

⚖️ Legal documents: Laws, contracts, court rulings, official call signs

💊 Medical advice: Diagnoses, treatments, medication dosages

💰 Financial calculations: Taxes, investments, accounting statements

🏗️ Engineering and construction: Structural designs, safety-critical calculations, infrastructure projects

📊 Critical business decisions: M&A, strategy shifts, large-scale investments.

Rule of thumb: If a mistake could cost money, reputation, or safety, AI alone isn't enough — expert verification is mandatory. 

These five clues help you catch a hallucination instantly.

#1. Overly specific details with zero sourcing

🚩 Red flag:

“According to a McKinsey study from March 2024, 67.3% of American companies…”

Why it's suspicious: Hyper-specific date + precise percentage + big-name source — yet no link, no title, no trace.

✅ What to do: Ask directly: “Please provide the source or a link to this McKinsey study.”

#2. Odd or generic-sounding name + legit-sounding title

🚩 Example:

“Professor John Smithson from Cambridge University, author of AI Revolution 2023…”

Why it's suspicious: The name reads like a placeholder, the book sounds plausible, but none of it shows up online.

✅ What to do: Look it up on Google Scholar, Amazon, or WorldCat.

#3. “Recent” events beyond the model’s knowledge cutoff

🚩 Example:

“In September 2024, OpenAI released GPT-6…”

Why it's suspicious: If the model's knowledge cutoff is April 2024, it can't know what happened in September.

✅ What to do: Ask: “What's your knowledge cutoff?” Then, verify using real-time news.

#4. Perfectly packaged numbers without any math

🚩 Example:

“Your project’s ROI will be 347%.”

Why it's suspicious: A supposedly exact number with no formula, no inputs, no logic — often a statistical guess.

✅ What to do: Request: “Please show the full step-by-step calculation.”

#5. Answers that feel too smooth or too ‘template-like’

🚩 Signs:

✅ What to do: Push for specifics: “Give real examples and cite your sources.”

How to prevent AI hallucinations? While you can't eliminate hallucinations entirely, you can significantly reduce them. Here are practical, proven strategies that help AI deliver more reliable, accurate outputs.

One of the most effective ways to reduce hallucinations is to narrow the model's task:

Clear boundaries guide the model, prevent unnecessary improvisation, and improve consistency.

Giving AI the correct information upfront is one of the most effective ways to prevent hallucinations. Here’s how to make that easier:

This approach ensures the AI has a solid evidence base before generating answers.

Templates help AI stay consistent and fact-based. Here's how to do it:

By guiding AI with structured templates, you make its outputs more accurate, transparent, and easier to verify.

Automated checks are essential for high-stakes tasks:

These measures act as safety nets that catch errors before outputs reach users.

You don't have to wait for advanced AI updates to reduce hallucinations. Try these methods now:

These strategies can be combined for even stronger results, giving you more reliable AI outputs immediately.

Designing the AI's workflow can drastically reduce hallucinations. Consider these approaches:

Structured architectures like these transform AI from a “guessing machine” into a system that consistently delivers evidence-based, trustworthy answers.

A reliable partner approaches AI not as a black box but as a system that must be designed, monitored, and continuously refined. They start by building a solid foundation: a verified, company-specific Knowledge Base that ensures the AI has access only to accurate, relevant information. Techniques such as Retrieval-Augmented Generation (RAG) enable the AI to reason over this data rather than guessing from general internet knowledge.

Beyond architecture, an expert partner integrates human-in-the-loop AI and processes for critical queries, adaptive routing to specialized models or tools, and continuous monitoring to keep knowledge up-to-date. They also apply fine-tuning and preference learning to encourage behaviors such as citing sources, refusing uncertain answers, and prioritizing accuracy over fluency.

The outcome is an AI solution that is transparent, explainable, and dependable. By partnering with an experienced AI developer, organizations can transform AI from a risky experiment into a strategic, reliable tool that enhances business outcomes, builds stakeholder confidence, and mitigates the AI hallucination risks.

At BotsCrew, we take AI reliability seriously — especially in industries like healthcare, finance, and legal services, where accuracy is critical. Our AI solutions are designed to provide trustworthy, evidence-based answers, reducing errors and building confidence in every interaction.

At the core is Retrieval-Augmented Generation (RAG). This method pairs the raw power of large language models with a verified Knowledge Base — a centralized repository containing only company-relevant, trusted information. 

Instead of guessing or “hallucinating,” our AI pulls answers directly from this source, ensuring every response is contextual, precise, and on-point. Unlike general-purpose AI like ChatGPT, the chatbot never mixes in external “world knowledge” that could lead to errors.

Another key safeguard is our knowledge verification system. If the AI can't find verified information for a question, it admits it openly rather than inventing an answer. And on top of that, technical guardrails prevent the chatbot from stepping outside predefined boundaries or producing unreliable content.

Before acting on AI-generated information, run through these steps: