Generative AI is a powerful ally that significantly reduces our workload, from writing and planning to summarizing and brainstorming. However, misuse can introduce inaccurate information, potentially causing problems and eroding trust.
The key lies in understanding the distinction between “what generative AI can do” and “what should not be entrusted to it,” and utilizing it at an appropriate distance. For experts to achieve results, the crucial factor is designing an operational framework that identifies AI’s strengths and weaknesses and clearly defines where responsibility lies.
With this premise in mind, let’s examine the mechanisms, strengths, and limitations of generative AI, and how professionals should engage with it.
What is Generative AI?
Generative AI refers to technology that learns from vast amounts of text, images, and other data to create new text, images, audio, and more based on input instructions. Large language models like ChatGPT are prime examples.
The Ministry of Education, Culture, Sports, Science and Technology’s explanation of AI is highly informative for understanding generative AI. It states that the term “AI” (Artificial Intelligence) was proposed by Dr. John McCarthy in the US in 1955, with academic discussions beginning the following year at the “Dartmouth Conference.”
It also states that there is still no internationally unified definition of AI. It is treated as a broad concept referring to “programs that operate in a manner similar to human thought processes” or “all information processing and technologies that humans perceive as intelligent.”
Source: Ministry of Education, Culture, Sports, Science and Technology, “Chapter 1: AI in a New Era”
Characteristics of Generative AI: "Strengths" and "Weaknesses"
Generative AI is a highly distinctive technology with both convenience and cautionary aspects. Let’s examine its strengths and weaknesses, exploring where it excels and where risks arise.
What Generative AI Excels At
The strength of generative AI lies not in simply “memorizing” vast amounts of knowledge, but in understanding context and organizing information in a way that fits the purpose. It excels at tasks like rearranging information and naturally smoothing out the flow of text, making it powerful for the following uses:
The key point here is that AI is not a “complete product creator” but an acceleration tool that shortens the time to completion. Human final judgment and editing maximize AI productivity.
Generative AI's Weaknesses and Limitations
Alongside its convenience, generative AI has five key weaknesses to be aware of.
① Hallucination (Plausible Lies)
Hallucination refers to the phenomenon where generative AI presents information it doesn’t inherently know as plausible facts.
The problem is that the text is often fluent and persuasive, making it easy to overlook these errors. Particularly, numerical data, proper nouns, regulations, and technical terms are prone to inaccuracies, requiring careful verification by the user.
② Vulnerability to Current Information and Specific Circumstances
Generative AI excels at general knowledge learned up to its training point but cannot handle “real-time information” like the latest news, legal revisions, internal rules, or specific project details. Discrepancies can occur where the AI states something as fact, but your organization’s rules differ.
Therefore, it is crucial to proactively feed it foundational materials like internal regulations, specifications, and past case studies beforehand.
③ Weak Verifiability
Even if a conclusion reached by generative AI appears correct at first glance, it may be unable to logically explain “why it arrived at that judgment.” In fields like auditing, healthcare, and legal work where providing evidence is essential, this lack of accountability poses a significant risk.
AI merely predicts patterns from vast data, making its reasoning process difficult for humans to trace. Therefore, outputs should never be submitted as final deliverables. Humans must always document the criteria used for judgments and the reference materials consulted.
④ Information Leakage and Confidentiality Risks
How information entered into generative AI is handled varies significantly depending on the tool used and the contractual arrangement.
For cloud-based services, there is always some risk that confidential information could be transmitted externally if entered by mistake.
Particular caution is required for handling medical, financial, R&D, and customer data. As countermeasures, strictly enforce rules such as redacting or anonymizing personal names and company names, and limiting use to approved internal environments.
The first step in risk management is developing the habit of constantly asking, “Is this information safe to show the AI?”
⑤ The Gray Areas of Copyright, Quotation, and Secondary Use
Text and images generated by AI are influenced by the vast datasets used for training, potentially producing expressions similar to existing works. This can lead to ambiguous copyright and usage boundaries, making it difficult to determine “what constitutes original content?” Particular caution is needed for commercial use and publications.
"Using Generative AI" vs. "Relying on Generative AI"
Considering the strengths and weaknesses outlined so far, the key to safely using generative AI is to treat it as an excellent assistant. It works quickly and formats text well, but it can mix in inaccurate information, leaving the final responsibility for accuracy with humans.
Understanding this premise clarifies that we don’t need AI to “provide the correct answer.” Instead, the right approach is to use it as a partner for quickly creating drafts that move work forward. This naturally clarifies the boundaries between tasks delegated to AI and those requiring human judgment, enabling confident utilization.
The Fail-Safe Model for Generative AI Operations
Generative AI isn’t a panacea, but by standardizing the workflow, you can ensure consistent quality while producing deliverables quickly. Here, we introduce five highly reproducible steps applicable to any task.
Step 1: Define the Purpose in One Line
If you’re unclear about what you want the AI to do, even carefully crafted instructions often yield responses that miss the mark. First, define “what you want to achieve with this task” in one sentence. This stabilizes AI output and brings results closer to your expectations.
Example:
- “Create a simple proposal memo for executives that presents the conclusion first.”
- “Anticipate points readers might misunderstand and compile them into FAQs.”
- “Rewrite medical information in plain language so beginners can understand it.”
Keep the purpose short and specific. Simply avoiding ambiguity here will significantly improve quality.
Step 2: Provide constraints upfront
Generative AI tends to deviate from intent when premises are ambiguous. Conversely, the more specific constraints you provide, the closer the output will align with expectations.
Main conditions:
- Word count (e.g., 300 words / 2000 words / 5000 words)
- Target audience (executives / field staff / general readers / beginners)
- Tone (Formal/Polite/Casual/Expert-level)
- Prohibited expressions (e.g., avoid definitive statements, prohibit medical recommendation phrasing)
- Topics to avoid (internal company matters, undisclosed information)
- Handling of evidence (e.g., “Based on primary sources,” “Prohibited from making assertions with ambiguous sources”)
The more constraints you provide, the more the AI will deliver outputs that meet expectations.
Step 3: Have the AI ask "confirmation questions"
Many failures stem from “making them start building right away.” By having the AI ask questions first before beginning work, you can significantly reduce discrepancies.
Recommended instructions:
- “Ask three questions about unclear points”
- “List the assumptions you understand”
- “List potential risks and points to note”
- “Organize the objectives and constraints before starting work”
This alone will significantly reduce failures. Think of it as a quick alignment with the AI before getting too close to the deliverable.
Step 4: Have it create output "in segments"
Generative AI tends to introduce more errors and break sentence structure when asked to produce large outputs at once. The most stable approach is to break the process into smaller steps.
Below is a recommended workflow for creating documents:
1. Create an outline
2. Key points for each heading (bullet points)
3. Create the body text
4. Refine expressions and unify tone
5. Final review
Rather than writing a long text all at once, completing it step by step dramatically improves both accuracy and speed.
Step 5: Final human review using a checklist
While AI-generated output is convenient, humans bear ultimate responsibility. At minimum, ensure humans check the following points: . “The final 5% is handled by humans” ensures both safety and quality.
Checklist Items:
- Proper nouns, numbers, dates (where AI makes the most errors)
- Definite statements (Are assertions made without basis?)
- Prohibited content (confidential information, medical recommendations, legally problematic expressions)
- Potential harm to readers (misunderstandings, misleading information, factual errors)
- Source/evidence validity (Does it contradict primary sources?)
Safe Use of Generative AI: Differentiating by Risk
To safely utilize generative AI, categorizing tasks into three levels—”low,” “medium,” and “high”—facilitates decision-making.
First, tasks involving public information—such as formatting text or correcting typos—are low risk and can be handled by AI.
On the other hand, proposals, customer-facing materials, and explanations involving numerical data fall under medium risk. Here, it is essential not to use AI output directly; humans must verify the basis and have a responsible party review it.
Tasks in healthcare, finance, or involving personal information are high-risk. AI should be limited to organizing points or drafting. Final decisions and accountability must always be handled by experts. Simply adhering to this standard significantly enhances the safety of AI usage.
Tips for Utilizing Generative AI in Healthcare Settings
To safely utilize generative AI in healthcare settings, it is crucial to position it not as a tool for making diagnoses, but as a tool supporting the decision-making process.
While AI excels at high-efficiency tasks like organizing large volumes of information, making comparisons, and creating explanatory materials, the final clinical judgment and explanations to patients must always be handled by experts.
Furthermore, defining the scope of information input into AI and strictly enforcing rules is essential. After implementation, sharing usage standards and verification systems among staff, and using AI not as an end in itself but as a support tool to enhance healthcare quality, will lead to safe operation.
Maximizing Potential: "Generative AI Isn't the Only AI"
While we’ve focused on generative AI thus far, maximizing AI’s value in practice requires another crucial perspective: the existence of “purpose-specific AI” optimized for particular specialized tasks.
While generative AI is highly versatile and convenient, specialized domains often require fixed input formats, strictly defined goals, and demand auditability and reproducibility. In such areas, purpose-built AI demonstrates greater strengths.
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