Prompt Engineering ( Stop Overprompting: Why Short AI Prompts Are Better Than Long Prompts ) has become a key skill for interacting with AI. It is about how to cleverly construct questions or instructions so that the AI ​​system can accurately understand and give the ideal response. However, this road of exploration is full of traps, and the quality and effectiveness of AI output will be affected if you are not careful. In-depth analysis of these common traps and mastering effective avoidance strategies are crucial to improving prompt engineering capabilities and fully realizing the value of AI.

1. Too vague: precise expression is key

When interacting with AI, one of the most common pitfalls is asking questions that are too vague. When questions are not clearly directed, AI responses are often broad and lack specificity, making it difficult to meet actual needs. For example, if you simply ask a command like "Tell me something," the range of possible responses from AI is almost infinite. It could be an anecdote, a piece of historical knowledge, or other irrelevant content. Such broad responses have little practical value to the questioner.

The root cause of this kind of problem is that the questioner fails to clearly define the needs. In daily communication, people are accustomed to relying on context and the other party's understanding ability to convey vague information, but AI does not have human perception and association capabilities, and it needs precise instructions to give an effective response. Take information retrieval as an example. If a user wants to know the weather in a specific city on a certain day, but only asks "how is the weather", AI cannot know the specific query location and time, and cannot provide accurate weather information.

The key to avoiding this trap is to make the questions more specific and clear. For example, "Tell me something" can be refined into "Please share with me the latest breakthrough in technological innovation", which clarifies the subject and scope of the information. When it comes to specific things, describe the key features as detailed as possible. If you want to know about a movie, you can't just say "Introduce me a movie", but should say "Introduce me a science fiction action movie released in 2023, starring Tom Cruise". In this way, give key information such as year, type and starring, so that AI can accurately filter out content that meets the needs.

2. Asking too many questions at once: Simplifying is more efficient

Asking too many questions to AI at once will make it "confused" and difficult to give a coherent answer. For example, a question like "Please explain quantum mechanics, its history, key figures, and current applications" contains many complex aspects. It is difficult for AI to explain each part in depth and accurately with limited output. The result may be that each point is only touched upon briefly, which cannot meet the questioner's demand for depth of knowledge.

This phenomenon occurs because when AI processes problems, it usually analyzes and generates answers according to preset algorithms and models. Too many questions at the same time will make it difficult for AI to determine the focus and logical order, resulting in confusing answers. This is like asking a person to complete multiple complex tasks at the same time without giving him a clear order and focus requirements. He is likely to be in a hurry and unable to complete the tasks with high quality.

To avoid such problems, complex problems should be broken down into multiple simple sub-problems. Taking "Explain quantum mechanics, its history, key figures, and current applications" as an example, you can first ask "What is quantum mechanics?" After getting a clear explanation of the basic concepts, you can then ask "What is the development history of quantum mechanics?", "Who are the key figures in the field of quantum mechanics?", and "What are the current applications of quantum mechanics?" This step-by-step questioning method allows AI to focus on each sub-problem and give more detailed and valuable answers, which also makes it easier for the questioner to systematically understand and learn relevant knowledge.

3. Inappropriate tone: It is important to adapt to the situation

If the tone of the question does not match the expected answer, it will also affect the response effect of the AI. Different tones are suitable for different scenarios and needs. If the tone is used improperly, the answer given by the AI ​​may not meet expectations. For example, when asking for weather information, using a formal tone "Could you please inform me about the current weather conditions in New Delhi?", you expect to get a formal and standardized answer such as "The current weather in New Delhi is 33 degrees Celsius with hazy conditions."; while using a casual tone "Hey, what's the weather like in New Delhi right now?", you would rather hear a more colloquial and friendly expression like "It's 33 degrees and a bit hazy over in New Delhi at the moment."

The tone problem occurs because AI's training data and models consider the style and context of the question when generating answers. If the tone of the question is inconsistent with the expected answer style, the AI ​​may not be able to accurately grasp the questioner's intention and give a suboptimal answer. In actual applications, this situation is more common in customer service conversations, content creation and other scenarios. For example, when creating novel dialogues, if AI is asked to generate casual conversations between characters in a formal tone, it will be difficult to get natural and fluent content.

In order to avoid tone issues, the usage scenario and expected answer style should be clearly defined before asking questions. If it is used for formal business reports, a formal and rigorous tone should be used; if it is used in daily communication or creative writing, a more relaxed and casual tone can be used. At the same time, some words or expressions that reflect the tone can be appropriately added to the questions to help AI better understand the needs. For example, when asking AI to write a humorous essay, you can add a statement such as "Please use humorous language" to guide AI to generate content that meets the requirements.

4. Insufficient details: Providing background is more accurate

When asking AI questions, omitting important details will prevent it from giving a good answer. For example, if you ask “how big is it?”, without knowing what “it” refers to, AI cannot determine whether it is asking about the size, area, or other related attributes of the object, and can only give vague or unsatisfactory answers.

This problem arises because AI does not have human common sense and contextual understanding. It can only analyze and answer questions based on the information provided in the question. In actual scenarios, questions that lack details will lead to inaccurate information and affect decision-making and judgment. For example, when consulting product information, if you only ask "how much is this product" without specifying the specific product name, AI cannot give accurate price information.

To solve the problem of insufficient details, sufficient background information must be provided when asking questions. For example, the question "How big is it?" can be changed to "How big is the Amazon rainforest?", which clarifies the specific direction of "it". In questions involving professional fields or specific situations, it is even more important to explain the relevant background knowledge and key information in detail. For example, in medical consultation, you cannot just say "What should I do if I have a headache?", but should add information such as the time and frequency of the symptoms, whether they are accompanied by other symptoms, so that AI can give more targeted suggestions.

5. Too high expectations: Recognizing boundaries is a prerequisite

Having unrealistic expectations for AI is also a common pitfall in prompt engineering (9 best prompt frameworks: Unlock the unlimited potential of LLMs). For example, asking AI to understand emotions and respond emotionally like humans, asking "How would you feel if you were in my situation?" This anthropomorphic way of asking AI ignores the fact that AI currently does not have real emotions and subjective consciousness. AI can only simulate human emotional expressions based on data and algorithms, but cannot truly empathize.

This phenomenon occurs because people do not fully understand the capabilities of AI. As AI technology develops, its performance becomes better and better, giving some people the illusion that AI is omnipotent. In practical applications, such high expectations may lead to incorrect use of AI and disappointment. For example, in a psychological counseling scenario, if a patient expects AI to provide emotional support and deep understanding like a real counselor, this may not be met.

To avoid such problems, we need to correctly understand the capabilities of AI. When asking questions, we should adjust our expectations based on the actual capabilities of AI. For emotion-related questions, we can change them to "How might an average person feel in this situation?" Such questions are more in line with AI's processing capabilities and can get more reasonable answers. At the same time, when using AI, we must be clear about its areas of expertise and limitations, and play to its strengths in appropriate scenarios.

6. Not learning from experience: Iterative optimization is the way

Repeated use of ineffective prompts without adjusting based on past experience will hinder the improvement of interaction with AI. For example, always asking AI to create creative content, but never adjusting the questioning method and requirements based on previous generated results, it is difficult to obtain higher quality creative results.

The root of this problem lies in the lack of reflection and summary of the interaction process. When interacting with AI, every question and answer is a learning opportunity, but many people do not make full use of this feedback information, resulting in repeated mistakes. In the fields of content creation, data analysis, etc., this situation will waste a lot of time and energy and fail to improve work efficiency and quality.

To solve the problem of not learning from experience, we need to attach importance to feedback and regard each interaction with AI as a learning process. After receiving the answer from AI, carefully analyze whether the answer meets the needs and where there are deficiencies. According to the analysis results, adjust the way, content and focus of the question. For example, if the article generated by AI lacks depth, you can add requirements such as "please analyze in depth" and "provide specific case support" when asking questions next time. Through continuous iterative optimization, the quality of questions can be gradually improved, so as to obtain AI answers that are more in line with expectations.

Prompt Engineering (compression of prompts in large models: making the most of every token) is an evolving skill. In the process of interacting with AI, we will encounter various traps. By identifying and avoiding the common traps mentioned above and continuously optimizing the questioning methods and techniques, we can better tap the potential of AI and achieve more efficient and valuable interactions.