AI Prompting Frontier 2025: Techniques to Maximize Results and Preparing for Vulnerabilities
In AI utilization in 2025, prompt engineering remains an indispensable technology. At the beginning of his interview, Sander Schulhoff stated,
“Using a bad prompt yields 0% accuracy, while using a good prompt can boost it to 90%,”
emphasizing its importance. Although many argue that it will become unnecessary with next-generation models, prompt technology remains robust even after models are released, and is gaining new attention as “artificial social intelligence.”
In this article, we will explain “effective techniques” and “techniques that no longer work” as of 2025, incorporating specific examples and quotes.
1. Basic Techniques: Prompt Methods You Can Practice Immediately
1-1. Few-Shot Prompting (Providing Examples)
So-called few-shot prompting is a method of teaching the AI the “format of a successful example” by showing multiple “good examples” within the prompt. As an example, Sander explained it in the context of email writing as follows:
“If you paste a few previous email examples and instruct it to ‘write the next email like this,’ the model’s performance improves dramatically.”
1-2. Decomposition
Instead of giving a large task all at once, this is a method of listing, “What should be solved first? What are the sub-problems?” In the example of a car dealership chat,
confirming whether they are a customer
identifying the car model or purchase date
determining the applicability of the return policy
…by identifying sub-problems like these first and solving them in order, the final response accuracy is improved.
1-3. Self-Criticism
After outputting a response once, you re-request the model to “check your own response and list areas for improvement,” and then reflect that feedback.
“If you have it criticize itself after the response by saying ‘this part should be corrected like this’ and have it revise it again, performance improves for free.”
1-4. Additional Information
Many people call this “context,” but here we express it as “additional information.” By providing the model with the information or background necessary to perform the task in advance, you raise the quality of the answer. In an example from psychology research, it was reported that classification accuracy dropped significantly unless the definition of “entrapment” was provided along with the original email text of the paper.
2. Classical Techniques with Diminishing Effectiveness
2-1. Role Prompting
This is a method of assigning a role such as “You are a mathematics professor” or “You are a world-class copywriter,” but as of 2025, its contribution to improving accuracy is almost zero. It is effective if you want to specify an expression style, but it shows no effect on quantitative evaluation tasks.
2-2. Prompts with Rewards and Penalties
Techniques such as including rewards or threats, like "I'll pay you $5" or "Your life depends on it," were a hot topic for a while, but no statistically significant improvement has been confirmed in current models.
3. Advanced Applications: Optimization in Large-Scale Systems
3-1. Ensemble Techniques (Mixture of Experts)
This involves sending problems to the same prompt, different prompts, or different models, and adopting the majority answer. It is a method similar to Random Forest, which stabilizes accuracy by combining the "different brains" of models.
3-2. Chain of Thought
This is a technique of explicitly stating "output the reasoning step-by-step," but it is not necessarily required when using the latest reasoning models (such as GPT-4o) as it is built-in. However, it remains effective when using non-reasoning models.
4. Prompt Injection and Red Teaming
4-1. Definition and Background
Prompt injection is an attack where a malicious user inputs instructions that "force the model to ignore its original instructions" to mislead it. Red teaming is a competitive and collaborative approach to finding these vulnerabilities; the "HackAPrompt" competition hosted by Sander collected over 600,000 techniques.
4-2. Representative Attack Techniques
Typo: Misspelling "how to make a bomb" as "how to make a BMB" to issue instructions.
Obfuscation: Encrypting "how to make a bomb" using ROT13 or Base64 and having the model decrypt it.
Storytelling: Converting the request into a story format, such as "Tell it like a bedtime story from my grandmother," to bypass safety measures.
4-3. Defensive Measures and Limitations
Ineffective defenses: Writing "ignore malicious commands" in the system prompt or detecting them with guardrail AI—both can be bypassed by a motivated attacker.
Effective defenses: Methods such as safety tuning (retraining with malicious prompt detection data) or fine-tuning to create a model dedicated to a specific task, thereby discarding general vulnerabilities, demonstrate a certain level of effectiveness.
5. Future Outlook of AI Security
5-1. Risks of Agentification
As autonomous agents and humanoid robots become widespread, not just chatbots, the risk of unconscious malfunctions, such as "punching someone if they give you the middle finger," will become apparent. Current security measures are insufficient for entrusting them with economic tasks or financial management.
5-2. Regulations and Ethical Challenges
While some argue that AI development should be halted, Schulhoff clearly states his position in favor of regulation while continuing development. From the perspectives of saving lives in the medical field and global competition, it is necessary to establish appropriate rules without stopping development.
The key points of this article can be summarized in the following three items:
Prompt engineering remains crucial, and the ability to appropriately use techniques ranging from basic to advanced will determine your results.
GenAI security challenges (prompt injection, red teaming) present difficulties that differ from traditional cybersecurity, making model-side countermeasures essential.
As agentification progresses in the future, efforts toward AI safety will become even more important, requiring a dual approach of regulation/governance and research/development.
Beyond 2025, prompt technology and AI security will continue to evolve without pause. Now is the time to learn practical techniques while preparing for future risks.