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.

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.

According to an IBM study, approximately 70% of generative AI users report having encountered hallucination issues. In other words, this is an unavoidable reality in AI development.

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.

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 often refers to outdated information from 2023 or early 2024.

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 December 2025.
4. Cross-check with multiple sources.

GenSpark has a fact-checking feature, but it is not yet at a level where it can be fully trusted. Final verification by a human is essential.

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.

Utilizing GenSpark's Fact-Checking Feature

GenSpark is characterized by always citing its information sources (source URLs) in its answers, which facilitates fact-checking. However, it's also important to understand the following limitations:

Advantages

• Sources are clearly indicated for all answers.
• Information is aggregated from multiple sources.
• You can click on links to check the original information.

Limitations

• Citations may not always be accurate.
• Possibility of referencing outdated information.
• Context may sometimes be distorted.

According to the latest survey in November 2025, the hallucination rate for generative AI is reported to be approximately 15-20%. This means that incorrect information may be present about once every five times.

Detailed usage will be explained in the next article, "Mastering GenSpark's Fact-Checking Feature."

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.

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.

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