Contrary to standard prompting advice that you should give LLMs the context they need to succeed, I find it’s sometimes faster to be lazy and dash off a quick, imprecise prompt and see what happens. The key to whether this is a good idea is whether you can quickly assess the output quality, so you can decide whether to provide more context. In this post, I’d like to share when and how I use “lazy prompting.” When debugging code, many developers copy-paste error messages — sometimes pages of them — into an LLM without further instructions. Most LLMs are smart enough to figure out that you want them to help understand and propose fixes, so you don’t need to explicitly tell them. With brief instructions like “Edit this: …” or “sample dotenv code” (to remind you how to write code to use Python's dotenv package), an LLM will often generate a good response. Further, if the response is flawed, hopefully you can spot any problems and refine the prompt, for example to steer how the LLM edits your text. At the other end of the spectrum, sometimes I spend 30 minutes carefully writing a 2-page prompt to get an AI system to help me solve a problem (for example to write many pages of code)…