[Letter from Andrew Ng] Lazy Man’s Tips: The Secret to Efficiency in the AI ​​Era

In the latest Andrew's Letters, Professor Andrew Ng published an interesting article " The Benefits of Lazy Prompting". He mentioned something quite counterintuitive: when asking questions to a large language model (LLM), you don't always have to explain the background clearly. Sometimes, just throwing a simple and crude prompt can solve the problem faster. This statement is completely opposite to prompt engineering, and it also made me curious, so I decided to read this article carefully, figure out what he is talking about, and share it with you.

What is a "lazy prompt"?

Let's first talk about what "lazy prompting" means. In simple terms, the instructions you give to AI are very short and without any details. You just throw it in and see what it can return. For example, you are a programmer, and your code is wrong and you get an error message. You just copy and paste the error message to AI without even saying "Fix it for me". What's the result? AI can usually guess that you want it to help analyze the problem and give a solution. This practice is "lazy prompting" - don't bother to organize the language, and rely on AI to understand it by itself.

Teacher Wu gave several examples in the article. For example, if you write "Edit this: ..." followed by a paragraph, AI will help you revise and polish it. Another example is that if you forget how to use a package in Python, such as dotenv, you can directly ask "dotenv sample code", and AI will give you a usable code. In this case, you don't say much, but AI can still give a reliable answer.

He thinks the biggest advantage of this method is that it is fast . You don't have to spend time writing long essays. You only need to input a few words in a few seconds and the AI ​​will start working. But there is a prerequisite: you have to be able to quickly see whether the answer given by the AI ​​is good or not. If the result is reliable, then everyone is happy; if not, you can slightly adjust the prompt word and try again.

When can I use lazy hints?

So when is it appropriate to be so "lazy" ? Teacher Wu mentioned that this trick is particularly suitable for those scenarios where you can quickly check the results . For example, when debugging code, you can tell whether it can be used by looking at the suggestions given by AI, or when you let AI edit a text, you can judge whether the changes are good or not by just glancing at it. In this case, lazy prompts can help you save a lot of time.

Another point is that this method is best used when you can quickly try and fail . For example, if you are operating on the AI ​​web interface and see that the result is not good, you can immediately change the prompt and ask again, which is very convenient. Teacher Wu emphasized that this is a process of "adjusting while doing", and as long as you can see the feedback immediately, lazy prompts are very useful.

When should you not be lazy?

Of course, not all situations can be handled so casually. Teacher Wu kindly reminded us of two types of scenarios where lazy reminders may cause you to fail.

The first one is , if you know that without some specific background, AI will definitely not be able to guess what you mean, then you have to write it clearly. For example, if you have a very special requirement, such as using a specific PDF to text tool (he also mentioned his team's LandingAI tool by the way, haha), you have to clearly state it in the prompt. Otherwise, AI may recommend other solutions that are completely different from your needs. At this time, being lazy will only waste time.

Second , if the answer given by AI is wrong, but you can't find the problem immediately, don't risk using lazy hints. For example, if you ask AI to write a long and complex code, it seems to be correct at first glance, but it takes several hours of testing to know whether it is correct. In this case, it is best to spend more time at the beginning to explain the requirements clearly and try to let AI get it right in one go, so as to avoid the trouble of rework later.

This is actually an "advanced technique"

Teacher Wu also mentioned that lazy prompts are not something that novices can easily master. He observed that most people give AI prompts that are too simple and not informative, which results in poor results. Therefore, he suggested that you first learn how to provide AI with enough background. After mastering this foundation, you can try to be "lazy" and see how many words you can say to make the AI ​​understand you. It's like a balancing game, and you have to slowly find the "just right" point.

Another small reminder: if you are writing code to call AI APIs, and it is a scenario where you run it repeatedly and cannot check the results every time, then don't use lazy prompts. Because you can't adjust it in time, the quality of the output must be guaranteed by detailed prompts.

An interesting metaphor

There is another highlight in the article. Professor Wu mentioned that the name of this "lazy hint" was thought up by his friend Rohit Prsad, and he also used the "lazy evaluation" in computer science as an analogy. What is lazy evaluation? It means not calculating until you really need the result. Lazy hints are the same: give the minimum instructions first, and if the AI ​​can't handle it, you can add some details. This metaphor is quite vivid, it feels like playing a "guessing mind game" with AI.

My thoughts

After reading this article by Professor Wu , I think he gave us a very useful idea. Usually we always think that the more detailed the communication with AI, the better, but sometimes we can relax a little and try a simple and rough way. Of course, this does not mean that you can do whatever you want, but you need to have judgment and know when to be lazy and when to be serious. As he said, "Keep building!" - keep trying and adjusting to find the method that suits you best.

In general, lazy hints are a very practical technique, especially for those who often use AI, which can save a lot of effort. However, it also has limitations. If used well, it can achieve twice the result with half the effort, but if used poorly, it may achieve half the result with twice the effort. Have you tried this "lazy" method? Welcome to leave a message to talk about your experience!