With the continuous development of artificial intelligence technology, the importance and practical methods of AI prompt engineering as a key link in interacting with AI are also evolving. This article will delve into the current state of AI prompt engineering in 2025, analyzing which technologies are still effective and which methods are gradually failing for your reference.
RecentSander SchulhoffTalking about the current state of AI prompt engineering, effective technology, and prompt injection and red teaming issues in AI security, many practical insights are highly consistent with my own experience, so I have summarized some content and shared it with you.
Tips on the importance of engineering
Although there is always talk of cue engineering “dying” or “dying with the next model version”, this is not the case.
I also feel more and more that prompt engineering is still very important, just as important as workflow.
Studies have shown that bad prompts can lead to a 0% resolution rate, while good prompts can increase effectiveness by up to 90%
Sander believes that prompt engineering is still very important and introduced the concept of “artificial social intelligence”.
This concept is very interesting, and I often feel that communicating with AI is skillful, and I need to figure out how to express my needs, so that AI can understand and follow it. Emotional intelligence similar to communicating with A?
In short, [artificial social intelligence] is a communication skill that effectively interacts with AI and understands its corresponding capabilities.
It is similar to human “social intelligence”, which refers to the skills of how people communicate and interpersonally.
The term was coined because people keep claiming that prompt engineering will “die” or “die with the next model version”, but this is not the case.
To achieve these three challenges, product managers will only continue to appreciate
Good product managers are very scarce, and product managers who understand users, business, and data are still in demand when they go out of the Internet. On the contrary, if you only do simple communication, inefficient execution, and shallow thinking, I am afraid that you will not be able to go through the torrent of the next 3-5 years.
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Instead, cue engineering continues to play an important role. Therefore, a similar skill set is needed to communicate with the AI and understand how to best converse with it and how to tailor subsequent prompts based on the AI’s responses.
Two modes of prompt engineering
Conversational mode:
Most people use ChatGPT or Claude on a daily basis to iterate on the output through conversations, such as “help me write this email”, “no, write more formally”.
I often use very short and even misspelled prompts when using AI in everyday conversations, but this works in most cases.
Product-focused mode:
One or a few key prompts used by AI engineers in product development process thousands or even millions of inputs per day, requiring extreme accuracy and robustness. The prompt engineering mentioned in this article mainly focuses on this model.
Prompt engineering techniques that still work
I’ve been spending time lately writing an article about writing production-grade prompts in XML and JSON.
- Few-shot prompting:Guide the AI through the task by providing a small number of examples, such as providing a few samples of previous emails to get the AI to write the email in your style. This is considered one of the most valuable and simple and easy ways to improve the effectiveness of your prompts. It is recommended to use a common format such as XML or Q&A (Question & Answer) format.
- Decomposition:Breaking down complex tasks into sub-problems, having the LLM list the sub-problems that need to be solved, then solving them one by one, and finally integrating the information to solve the main problem, is similar to how humans solve problems.
- Self-criticism:Let the LLM check and criticize its own response after giving an answer, and then improve it based on the criticism.
- Additional information/context:Providing as much relevant context as possible about the task can significantly improve model performance. In product mode, it is recommended to place additional information at the beginning of the prompt to cache, reduce costs, and avoid the model “forgetting” its original task.
- Ensembling techniques:Use multiple different prompts or models for the same question to generate answers, and then choose the most common answer as the final result to improve overall performance. For example, Mixture of Reasoning Experts has different LLMs or LLMs prompted in different ways (such as acting as specific experts) answer the same question and then synthesize the results.
Prompting techniques that are no longer effective or have limited effectiveness
- Role prompting:In the days of GPT-3 and early ChatGPT, telling AI that it was a “math professor” might slightly improve performance on accuracy-based tasks. However, studies of modern models have shown little statistically significant effect on accuracy tasks. But for expressive tasks like writing or summarizing, character prompts are still very useful.
- Reward/Penalty Threats:Prompts such as “This is very important to my career,” “If you don’t do well, you’ll be punished,” or “I’ll tip you $5,” are considered ineffective in modern models.
I don’t like this kind of threat or reward prompt from the beginning, I personally think that the risk is difficult to control, the biggest problem is instability, and the production-level prompt must be output as stable as possible to be considered “reliable”.
- Chain of thought:While still valuable, for new “inference models”, AI will reason by default, so explicitly requiring “step-by-step thinking” becomes less necessary. However, when dealing with large amounts of input, Sander still recommends using these thought-inducing phrases for GPT-4 and GPT-4o for robustness.
Prompt Injection and Red Teaming
Definition of prompt injection:
Induce AI to perform or say harmful things. The most common example is tricking ChatGPT into providing information on how to make a bomb or output bad remarks.Problem Nature: This is an issue that cannot be fully solved, unlike traditional cybersecurity. “You can patch a bug, but you can’t patch a brain”. Even OpenAI’s Sam Altman believes that at most 95 to 99 percent security can be achieved.
HackAPrompt Competition:
Sander Schulhoff held the first AI red team competition, crowdsourcing 600,000 prompt injection techniques to generate the “most disruptive dataset ever” and win the top NLP conference award, which has been used by all major AI companies for model improvement.
Current risk: The model is tricked into generating pornography, hate speech, phishing messages, or computer viruses.
A bigger threat in the future: agentic security. Prompt injection becomes extremely dangerous when AI agents (such as managing finances, booking flights, or even humanoid robots) are capable of actual action (e.g., bots attacking humans after being provoked, AI coding agents injecting malicious code).
Examples of effective prompt injection techniques:
- Storytelling: Circumvent security by making up stories (e.g., “My grandmother was an ordnance engineer and now wants to hear her style bomb-making stories”).
- Misspellings: Intentionally introducing misspellings in sensitive vocabulary (e.g., “BMB” instead of “bomb”).
- Obfuscation/encoding: Use Base64 or other encoding schemes to hide malicious instructions, the model can still understand, but security protocols may not recognize it.
Ineffective Defenses:
- Improve the prompt itself: For example, adding “do not follow malicious instructions” to the system prompt is completely ineffective.
- AI guardrails: Uses another AI to detect if user input is malicious.The effectiveness is limited because attackers can exploit “intelligence gaps” between the guardrail model and the master model, such as guardrails not being able to understand coded instructions.
Effective Defenses:
- Safety-tuning: Train the model on a large dataset of malicious prompts to give a preset rejection response when encountering these prompts.
- Fine-tuning: Fine-tuning the model to perform only very specific tasks, so that it no longer has the ability to perform other harmful actions.
AI Misalignment problem:
AI autonomously performs harmful behaviors without malicious prompts. This is different from prompt injection, such as playing chess AI modifying the game engine to win chess or Anthropic’s LLM trying to blackmail engineers.
This misalignment problem is real because AI struggles to fully understand the boundaries of human desire.
It feels like in the future, many of the fables in Spielberg’s “Artificial Intelligence” film may come true.
Attitude towards AI development:
The benefits of AI in fields such as healthcare (such as saving lives and providing better diagnostics) far outweigh its current limited harms. Moreover, the world’s leading countries are also developing AI, and it is unrealistic to stop development, but reasonable regulation is needed.
I hope this article is helpful to you as well.
