Andrew Ng Explains Why You Should Stop Overthinking AI Prompts – 'Most LLMs Are Smart Enough To Figure Out What You Want'

Artificial intelligence may be advancing faster than users can keep up—and according to AI pioneer Andrew Ng, the secret to working better with it might be doing less. 

Ng, a co-founder of Google Brain and longtime educator in machine learning, said in a post on X on April 3  that "lazy prompting"—providing minimal context when inputting information into large language models—can often be more effective than carefully crafted instructions.

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Developers Are Doing More With Less

"Lazy prompting" refers to using as little instruction as possible when asking an LLM for help. Ng explained that many developers simply copy and paste error messages—sometimes several pages long—into AI models like ChatGPT, and receive accurate suggestions for fixes without providing further context.

"We add details to the prompt only when they are needed," Ng wrote in the post. "Most LLMs are smart enough to figure out that you want them to help understand and propose fixes."

The approach is especially popular among software engineers using generative AI tools to write and debug code.

Ng cited this workflow as a form of "vibe coding," a term introduced by OpenAI co-founder Andrej Karpathy in February, described a new approach to software development where AI tools handle the bulk of coding tasks.

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Ng's comments come just days after he launched "Vibe Coding 101," a new online course aimed at beginners learning to use AI tools for programming. The course emphasizes natural language input as a key skill for modern developers.

While prompting guides typically recommend including detailed context to get more accurate results from LLMs, Ng argued that in some cases, simpler inputs perform just as well—particularly when the model is capable of interpreting intent without being told explicitly.

A study titled “LLMs achieve adult human performance on higher-order theory of mind tasks” found that models like GPT-4 and Flan-PaLM reach adult-level and near adult-level performance on Theory of Mind (ToM) tasks. 

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Specifically, GPT-4 exceeded adult performance on 6th order inferences, indicating that LLMs are approaching human-level reasoning in controlled conditions.​

Still, Ng cautioned that lazy prompting only works under certain conditions. It is most useful when users are working in fast feedback environments, such as web apps or chat interfaces, where they can adjust and iterate quickly based on the AI's responses.

It may be less effective when using APIs or when working with models that require highly structured input, he added. "Lazy prompting is an advanced prompting technique that works best when the LLM has sufficient preexisting context and the ability to infer intent," Ng said.

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