Introduction

"If AI says it, it must be correct"—do you think so? When you first start AI development, it's easy to trust AI's answers without question. However, in actual development environments, there have been reports of information confidently presented by Genspark being incorrect.

This time, I'll share my experiences with the "AI hallucination" problem encountered in AI development and discuss countermeasures.

Update: September 2026: Genspark has changed considerably since this article was first published in 2025. It is now much more than a simple AI chat or search service, with Super Agent able to browse the web, use tools, work with files, and carry out multi-step tasks. However, the basic point of this article has not changed: AI output still needs to be verified.

For a current overview of the service, see "What Is Genspark? What You Can Do, Its Benefits, and What I've Learned After 18 Months of Use".

What is the AI Hallucination Problem?

AI hallucination is a phenomenon where AI confidently outputs information that is not based on facts, as if it were accurate information. Large Language Models (LLM) generate "plausible" answers from learned data patterns, but they don't necessarily verify facts.

IBM's current explanation of AI hallucinations, updated in February 2026, still describes the problem as AI producing inaccurate or nonexistent information as if it were real. In other words, hallucination is still something AI users and developers need to account for.

For a more detailed explanation, see "AI Hallucination: What AI Beginners Should Know First".

One point I would describe more carefully today is that not every incorrect AI report is technically a hallucination. For example:

  • Inventing a company, statistic, or fact is a hallucination.
  • Saying "I saved the file" when the file was not actually saved is better described as an incorrect completion report.
  • A tool failing while the AI believes the task succeeded is an agent execution or verification failure.

From the user's point of view, all of these can feel like "the AI lied," but the underlying cause is not always the same.

Experience 1: The "I Thought I Saved It" Incident with Twitter API Credentials

During the development of a fortune-telling website, the most memorable trouble I encountered was related to Twitter API authentication information.

Genspark's Statement: "I have saved the Twitter API keys to environment variables. OAuth 1.0a authentication should now work correctly."

In such cases, deploying the code can result in a 401 error, causing authentication to fail.

Upon Investigation, the Causes Were:

  • The environment variable file had not actually been updated.
  • The AI only provided a code example for "saving," but the actual saving process was not performed.
  • The media_data parameter, required for OAuth 1.0a signing, was missing.

Genspark may report "task completed," but unless you actually open and check the file, you won't know if it was truly saved.

Experience 2: "I Found a Problem!" But...

Even more troublesome are cases where the AI reports, "I found a problem and fixed it."

During development, Genspark said: "I found a bug in the OAuth authentication flow! The signature generation order was incorrect, so I fixed it."

However, looking at the actual code...

  • The code before and after the fix was almost identical.
  • The "problem" pointed out did not actually exist.
  • In fact, the fixed code introduced new bugs.

The AI believed it had "found a problem," but nothing was actually improved. If this happens multiple times, development efficiency significantly decreases.

Experience 3: The Company List Dummy Data Problem

To write a feature article about GenSpark, I once had the AI collect a "list of Japanese AI startup companies."

Genspark confidently presented a list of 20 companies, but upon fact-checking...

  • 5 companies were fictitious names that didn't exist.
  • 3 companies had already gone bankrupt.
  • The location or founding year for 4 companies was incorrect.

Genspark stated, "I created this based on the latest information," but in reality, it contained a mix of old and fabricated information. If this had been used directly in an article, it would have led to a significant credibility problem.

The Trap of Online Information Gathering: Reliance on Outdated Information

Even if you ask AI to "research the latest information," it can still refer to outdated information.

Genspark's current Super Agent supports live browsing, but that does not mean every page it finds is current. Search results themselves can contain outdated documentation, old blog posts, or pages that have not been updated.

How to Check

  1. Always verify the URL of the information source cited by the AI.
  2. Check the publication and update dates of articles.
  3. Verify if the information is truly up-to-date as of September 2026.
  4. Cross-check with multiple sources.

Genspark now has a dedicated AI Fact Checker that searches multiple sources and provides evidence for its verdicts. This is much more advanced than the fact-checking workflow available when this article was first written.

However, final verification by a human is still important, especially for information involving prices, specifications, legal matters, company information, or other facts that can change quickly.

Trusting Yet Doubting AI: Practical Countermeasures

So, how can we discern AI's fabrications? Here are some practical countermeasures.

1. Always Verify Critical Tasks

  • File saving: Actually open the file and confirm its contents.
  • API settings: Run tests to confirm operation.
  • Database changes: Verify directly with SQL.

2. Don't Blindly Trust AI's "Completion Reports"

  • "Saved": Verify if it was actually saved.
  • "Fixed": Check the differences to see what changed.
  • "Found a problem": Verify if it was truly a problem.

3. Request Concrete Evidence

  • "Which file was it saved to?"
  • "Which line numbers were changed?"
  • "From which source was the information obtained?"

Genspark's answers are "suggestions," not "absolute truths." Humans must always verify critical tasks.

4. For Important Work, Have Another AI Review It

This is something I use more often now than when I first wrote this article.

If the same AI creates something and then reviews its own work, it may repeat the same assumptions or overlook the same problems.

I now sometimes have code created with Claude Code reviewed by Codex or Antigravity. In actual testing, the different AIs found different kinds of problems.

I wrote about that experience in "I Had Codex and Antigravity Audit Code Built with Claude Code — Right Now, I Don't Think You Should Rely on Just One AI".

Utilizing GenSpark's Fact-Checking Feature

Genspark now provides a dedicated AI Fact Checker.

According to Genspark, the tool searches multiple web sources, takes screenshots of evidence pages, and uses another AI model to confirm whether the screenshot actually supports or contradicts the claim.

It then returns a verdict such as TRUE, FALSE, or PARTIALLY TRUE, along with source URLs, direct quotations, and screenshot evidence.

This makes fact-checking easier than it was when this article was originally published. However, it's also important to understand the following limitations:

Advantages

  • Information is checked against multiple sources.
  • You can click on links to check the original information.
  • Screenshot evidence can be inspected.
  • The evidence is checked again to see whether it actually supports the claim.
  • It can return structured TRUE / FALSE / PARTIALLY TRUE verdicts.

Limitations

  • Citations may not always be accurate.
  • Possibility of referencing outdated information.
  • Context may sometimes be distorted.
  • Fact-checking AI is still AI, so it should be treated as a verification aid rather than an infallible authority.

The previous version of this article stated that the hallucination rate for generative AI was approximately 15-20%.

I have removed that figure because hallucination rates vary widely depending on the model, task, benchmark, and definition being used. Treating one percentage as the hallucination rate for all generative AI is misleading.

The more useful rule is simple: assume that any important AI-generated factual claim may need verification.

Summary: AI is a Powerful Assistant, But Humans Make the Final Decision

Genspark is a powerful tool that significantly accelerates development. However, it is not perfect.

Key points to remember:
  1. AI's answers are "suggestions," not "absolute truths."
  2. Humans must always verify critical tasks.
  3. Utilize the fact-checking feature as a supplementary tool.
  4. Always check whether outdated information is being referenced.
  5. Be cautious of dummy data and fictitious information.

Generally speaking, the most important thing is to maintain a healthy skepticism while trusting AI. We recommend utilizing AI Drive's specification saving feature to always save critical information in a way that humans can verify.

Genspark has become much more capable since this article was first published, but greater autonomy actually makes verification more important in some situations. The more work you delegate to an AI agent, the more important it becomes to check the final output rather than relying only on its "completed" message.

Next time, we cover the theme "Genspark Forgets Quickly," discussing issues arising from long-term use of the chat interface and countermeasures using AI Drive.

For pricing details: Genspark Pricing Page (pricing details are further down the page)