Lazy Prompt Method: Andrew Ng proposed a new AI interaction trend. The simpler the prompt words, the better the effect.
The lazy prompt method proposed by Andrew Ng simplifies AI interaction and improves efficiency.
Core content:
1. The basic concept and advantages of the lazy prompt method
2. Comparison with traditional prompt engineering
3. Application scenarios and examples suitable for the lazy prompt method
With the development of the reasoning capabilities of large language models (LLMs), AI developers and users' understanding of prompt engineering has also changed accordingly. Traditionally, prompt engineering emphasizes providing detailed context and clear instructions for LLMs to ensure that the model generates high-quality output. However, a new trend called lazy prompting has recently begun to attract attention. It advocates the use of minimal or imprecise prompts and relies on the reasoning ability of the model to infer user intent. This method not only simplifies the interaction process, but also may improve efficiency, and is particularly suitable for the rapid implementation of tasks.
What is Lazy Prompting?
Lazy Prompting refers to providing minimal information or questions when interacting with LLMs, allowing the model to understand and generate responses on its own without the need for complex prompt design.
This concept was first proposed by AI expert Andrew Ng in his open letter The Benefits of Lazy Prompting [1] , and received heated discussion and positive reviews in the AI community after he forwarded it to X [2] . Andrew Ng pointed out that in some cases, using short, imprecise prompts can get useful output faster, especially when users can quickly evaluate the quality of the output and iterate . This approach relies on the powerful language understanding and reasoning capabilities of LLMs , which can infer the user's true needs from limited input.
Similar to the lazy prompting method, Daniel Nest proposed another related concept , “No-Prompt Prompting” in his March 20 article No-Prompt Prompting? So Lazy, It Just Might Work! [3] . He believes that when the context is clear enough, such as describing an image or analyzing a dataset, LLMs can infer the task without prompts . This is similar to the lazy prompting method, both emphasizing the model's autonomous reasoning ability .
Why is Lazy Prompting a trend?
The reasons why the lazy man's tip method has received widespread attention and praise may include the following aspects:
Efficiency: Traditional prompt engineering may require users to spend a lot of time designing and optimizing prompts, while the lazy prompt method allows quick interaction, saving time and effort. For example, when debugging code, developers only need to paste the error message and the model can provide suggestions without additional instructions. Accessibility: For users who are not familiar with prompt word engineering, the lazy prompt method lowers the threshold for using AI, making it easy for more people to take advantage of the power of LLMs. This reduces the reliance on the skills required of so-called "prompt sub-engineers". Model capabilities: Modern LLMs have increasingly enhanced language understanding and reasoning capabilities, and are able to infer intent from minimal input. LLMs can handle ambiguous input in some scenarios .
What scenarios are suitable for lazy prompting?
Here are some specific applications of the lazy man's tips:
Error repair: Many developers (including me) directly paste several pages of code errors into the AI model during code development without any additional instructions. Currently, the mainstream large models can directly propose repair solutions without additional prompts. Code generation: Users can simply request “write a Python function to calculate the Fibonacci sequence” or “write a front end for a cryptocurrency trading software” without providing detailed function signatures or implementation details. The model can generate reasonable code based on the context. Content analysis: Users upload a data set or an image and let the AI model automatically analyze, describe or classify it without specifying a specific analysis task. For example, the article No-Prompt Prompting? So Lazy, It Just Might Work! [3] mentioned that by directly uploading the Titanic data set, the model can automatically provide analysis results. Creative Exploration: With fewer prompts, AI models may generate unexpected creative outputs, helping users discover new ideas or solutions.
Lazy prompting also has limitations
Although the lazy tip method has its advantages, there are some situations where it is not appropriate:
Complex tasks: For tasks that require specific context or detailed instructions, lazy prompting may not provide enough information, causing the model to generate inaccurate or irrelevant output. In particular, when the prompt word needs to provide data or output examples, it is still necessary to follow the traditional prompt word engineering method and clearly state it in the prompt. Outputs that are difficult to verify: If it is difficult to quickly assess the correctness of the AI output, using the lazy tip method may waste more time on checking and correcting. For example, running the code verification function may take a lot of time, and Andrew Ng recommends providing more context in this case. Specific requirements: When a task has specific requirements or requires a specific approach, such as when the task involves a specific tool (such as PDF-to-text conversion software), the lazy tip method may not work and the user needs to clearly indicate this in the tip.
Best Practices and Usage Recommendations
Whether it is structured prompt words, no prompt words or lazy prompt method, they are all for improving our work efficiency. Therefore, Andrew Ng proposed the following best practices for lazy prompt words:
Start with minimal prompts: Try the simplest prompts first to see if the AI model can understand and generate a useful response. For example, start with the simplest request like “Edit this”. Quickly evaluate outputs: Choose tasks where you can quickly judge the quality of the output and make clear your task objectives so you can decide in a timely manner whether you need to provide more context. Be prepared to iterate: If the initial output is not ideal, be prepared to optimize the prompt by adding more details or instructions. This seemingly simple technique is actually an advanced prompt word technique that requires the user to be able to iterate and optimize.
Less is More!
With the continuous exploration of Scaling Law in the field of AI, the capabilities of large models are becoming stronger and stronger. The trend of Less is More has also swept the field of AI development and application . As an emerging AI interaction trend, the lazy prompt method provides users with an efficient and easy way to utilize the powerful reasoning capabilities of large language models. Although it is not suitable for all scenarios, for experienced users, being able to use the lazy prompt method at the right time will greatly improve work efficiency.
As AI technology continues to advance, lazy prompting is expected to play a greater role in the future and become an important skill in AI applications. In an interview with Business Insider, Andrew Ng Explains Why 'Lazy Prompting' Can Be a Useful AI Technique [4] , he also mentioned that this method may become more and more effective as the model becomes smarter.
