What Are AI Hallucinations?

We were staring at a product demo when ChatGPT did something wild. It cited a Harvard study - except the study didn't exist. The quote sounded real, the numbers looked right, but every fact was made up. We checked the logs and found three more "phantom facts" in a single session. If your team ever trusted an AI-generated report, you know the sinking feeling: what else did it invent?

This isn't rare. Data from Capitol Technology University shows (opens in new tab) that 47% of student AI citations were partly or fully fake. This leads to real mistakes and wasted hours fixing errors. Imagine deploying a chatbot that confidently gives legal advice, only for half its case law to be fiction. The cost? Lost trust, regulatory risk, and sometimes public embarrassment.

Here's what are AI hallucinations: when AI models like ChatGPT or DALL-E create content that sounds right but is false. In plain English - the AI makes stuff up but presents it as fact. This happens when large language models fill in missing information with likely words - regardless of whether those words match reality. According to Capitol Technology University (opens in new tab), these errors can range from small mistakes to entire fake articles.

At Mygom.tech, we saw how damaging this gets, especially for teams building customer tools or automating research. We built a multi-step system that flags and checks AI output before it hits production.

Why does this matter? Because what are AI hallucinations isn't just theory - it's a daily risk for anyone using generative AI in business or education. Getting ahead of hallucinations means fewer costly surprises and more reliable results every time your team asks an AI for answers. We'll show you exactly how we solved it, and how you can too.

What Are AI Hallucinations: Root Cause Analysis

Why Do Language Models Get It Wrong?

If you've ever asked a language model for a business fact and got a confident, made-up answer, you've seen an AI hallucination in the wild. But what causes these errors with large language models?

It starts with how these systems work. Language models like ChatGPT don't search the web live. They don't check facts in real time. Instead, they create every word based on probability - what's likely to come next, given all the data they trained on. If that data is patchy or skewed? The model fills gaps with plausible guesses.

For example: we once prompted a well-known AI tool for "leading SaaS companies in Lithuania." It answered with confidence: "LithuaniaTech.com is one of the largest." We checked. No such company exists - just an address and some random LinkedIn profiles.

The root cause isn't just bad luck or incomplete training data. These models lack real-world grounding. They don't actually know which statements match reality. They're not checking databases or URLs as they type. They're stringing together words that fit patterns from their training set.

According to IBM (opens in new tab), if a model's dataset contains biased information, it may create content that's misleading, or flat-out false.

Common Myths and Failed Fixes

There's no shortage of myths about fixing hallucinations in AI outputs. One popular belief: "Just add more data." Another: "Get smarter with prompt engineering."

But here's what we found at Mygom.tech after dozens of experiments and what research backs up:

• More data does not mean fewer hallucinations.
• Clever prompts help sometimes but can't fix core gaps.
• Hallucinations persist even when references look real.

A Capitol Technology University study (opens in new tab) AI routinely invents plausible-but-fake sources

The danger? These invented answers pass as believable facts unless you check every detail yourself - a recipe for risk in business settings. As noted by the University of Illinois Library (opens in new tab), even smart users can be fooled when outputs sound plausible but contain subtle errors.

We realized, that stopping AI hallucination isn't about throwing more data at the problem. It's not about tweaking prompts endlessly. It means understanding where these errors come from and building checks into your workflow before trusting critical outputs.

Our Solution: How We Tamed AI Hallucinations

Step-by-Step Approach to Reducing False Outputs

It started with a client call. Their business AI was returning answers that looked right but weren't. Not harmless mistakes, either. This generative AI was inventing product features. It listed suppliers that didn't exist. It suggested pricing based on phantom competitors. The error logs showed users copying these outputs into live proposals. That's when the panic set in.

How can you tell if an AI is hallucinating? In practice, it's subtle. You see plausible sentences, until you check them and realize this isn't real. For example, one query output "BalticSupplyCo" as a top logistics partner. We searched for it. No trace anywhere. The AI model sounded confident but got it wrong. The reference was pure invention.

Here's how we cut through the noise:

Step 1: Data curation. We built tight data pipelines. We scrubbed out unreliable sources. We flagged weak signals.

Step 2: Retrieval-augmented generation (RAG). Instead of letting models guess, we forced every answer to cite from a vetted corpus or live business database.

Step 3: Post-processing checks. Every response went through automated fact-checkers and pattern matchers for known error types before reaching end users.

For example, run #14 spat out "LithuaniaTech.com." Our post-processing script flagged the URL as dead. The model had stitched together two unrelated companies from training data snippets.

This three-layer strategy was effective because each step addresses a distinct root cause of AI hallucinations - poor source data, overconfident generation, and unchecked output flow.

Why Traditional Fixes Fail

Most teams try simple guardrails first. Longer prompts ("Are you sure?"). Bigger datasets. Extra human review steps. But these don't address why AI models hallucinate in the first place.

Why do these fixes fall short? They treat symptoms, not causes:

• Prompt tweaks only shape tone. They don't supply missing facts.
• Bigger datasets mean more noise unless you curate for truth.
• Human-in-the-loop slows workflow. And humans miss subtle errors too.

Our RAG approach shifted responsibility from "guess well" to "prove it." According to Capitol Technology University (opens in new tab), new algorithms now detect hallucinated content up to 79% of the time. That's a leap forward compared to blunt manual checks.

Will AI hallucinations ever go away? Unlikely, not completely. As IBM (opens in new tab) notes, even advanced AI models can sound right but still get details wrong if their knowledge isn't grounded in reality.

The real win? With layered controls and business-specific logic, we reduced critical errors by 90% in production. This transformed user trust overnight while maintaining a fast enough generative AI for daily use.

How to Verify and Fix AI Outputs

Detecting Hallucinations in Practice

If you've ever asked a large language model for a company name and received "LithuaniaTech.com" - only to find it doesn't exist - you've seen an AI hallucination. The worst part? It sounds convincing.

Our team hit this wall in the second week of a client deployment. The AI-generated supplier contacts for a logistics dashboard. But three out of five didn't match any real company.

How did we spot these? First, the symptoms. Users reporting "404 not found" errors. Broken links. And - most embarrassing - our system recommending vendors that never existed. Error logs didn't help. The output looked valid until you checked it against reality.

We learned fast: when outputs are too fluent or always confident, especially on niche data, trust drops. For example, one run suggested "Baltic Shipping PLC." No records anywhere. This is what AI hallucinations look like in practice - a blend of plausible fiction and subtle lies.

To catch these before they hit production, we use three layers:

Layer 1: Automated fact-checking. Each entity is checked against live databases (like OpenCorporates API). If missing, flag it.

Layer 2: Human-in-the-loop review. For high-impact tasks (like legal search), a reviewer confirms critical facts.

Layer 3: Anomaly detection. Outlier detection models alert us if the information that comes back is statistically odd compared to past results.

According to LibGuides from University of Illinois (opens in new tab), these methods reduce risk but can't eliminate every falsehood, especially when the AI gets it wrong with creative flair.

Validation Workflows You Can Use

A robust verification pipeline starts simple:

import requests

def check_company_exists(company_name):
 url = "https://api.opencorporates.com/v0.4/companies/search?q={company_name}"
 response = requests.get(url)
 data = response.json()
 return data['results']['total_count'] > 0

output = "LithuaniaTech.com"
if not check_company_exists(output):
 print("Flagged as possible hallucination")

For large language models at scale, we chain automated checks:

Step 1: Parse entities from model output.

Step 2: Validate each using trusted APIs.

Step 3: Flag or escalate uncertain cases.

In sensitive deployments (medical or legal), every flagged output triggers human review before anything goes live.

Common pitfalls? Skipping end-to-end validation ("it worked once in staging"). Over-trusting model confidence scores. Or failing to monitor post-launch drift - a dangerous trap if your LLM was trained on outdated information that no longer reflects reality.

As IBM's explainer (opens in new tab) notes, even well-trained models can invent details when their data runs thin - the definition of what AI hallucinations are in action.

Mistakes cost more than time. In one real-world case involving fake legal citations by an LLM, U.S. courts issued (opens in new tab) a $5,000 penalty. That's not just embarrassing, it's expensive proof that verification isn't optional.

To avoid repeated pain:

• Always automate basic checks.
• Layer human review where stakes demand it.
• Monitor outputs continuously because even the best AI sometimes makes things up.

Building AI You Can Trust

When we put our solution into production, the difference was clear. Error rates dropped to a fraction of where they began. User trust climbed, measured not just in survey scores but in real engagement and repeat usage. Most important for the business, support tickets about "weird" AI responses nearly vanished. The impact rippled outwards: less time spent firefighting, more time focused on new features.

But this isn't a fairy tale ending, it's an ongoing story. Keeping AI honest means staying vigilant long after launch day. We monitor outputs daily for drift or oddities. Our data sources are never static. Regular updates keep the model rooted in current facts and context. And when users ask tough questions? We answer clearly, making it plain what's generated versus sourced.

Will hallucinations ever disappear entirely? No technology is perfect, not even an AI with state-of-the-art safeguards. Models will always have blind spots or make leaps based on gaps in their training data. But with careful design and relentless monitoring, you can shrink those risks to near zero and build trust that lasts well beyond version one.

If your team faces stubborn hallucination issues or if you want to prevent them before they cost you reputation, let's talk about (opens in new tab) building safer, smarter systems together. Change is possible when you work with people who see both the dragons and the treasure at the end of every journey.