I added one line to my Codex prompts, and it fixed the tool's biggest problem

Published Aug 17, 2026, 10:30 AM EDT Mahnoor Faisal is a tech journalist covering AI and productivity tools with bylines at XDA, SlashGear, MakeUseOf, Laptop Mag, and Android Police. She's been writing professionally since she was sixteen, and has since penned hundreds of articles. This includes in-depth coverage of AI tools like NotebookLM to breaking news across the AI space. Her passion for technology started when she received her first iPod Touch (4th generation) on her 8th birthday, and she's been deep in the tech world ever since. Currently pursuing a degree in computer science, Mahnoor brings both a journalist's eye and a technical foundation to her coverage of how AI is reshaping the way we work and learn. As someone who spends a lot of time using AI and having conversations with people who use AI in different capacities, I've now realized that many think switching LLM providers is the only way to get better results. If ChatGPT isn't giving them what they want, they move to Claude. If Claude starts frustrating them, they try Gemini. Similarly, when a coding agent isn't behaving the way they'd like, the instinct is often to replace the tool entirely. I've found that the problem isn't always the model, though. Sometimes, a tiny change in the way you phrase your prompt can completely change how an AI approaches a task. I've seen this happen with Claude, Claude Code, NotebookLM, ChatGPT, and recently, I found an equally small tweak that fixes what I think is Codex's biggest flaw. Codex has a habit of doing too much Codex, nobody asked While Codex wasn't all that impressive initially, I gave it another chance a few months ago when Anthropic began making a few controversial choices around Claude Code. By that point, Codex had improved significantly, and it didn't take long before I started using it much more regularly. The more time I spent with it, though, the more I noticed that Codex has a tendency to do far more than I actually asked it to. I would give it what I would describe as a relatively straightforward task, only to come back to a much bigger change than I expected. Instead of making the smallest adjustment needed to solve the problem, Codex would sometimes refactor surrounding code, introduce new abstractions, add safeguards for edge cases that weren't particularly relevant, or make other "improvements" I never asked for in the first place. This is much more prevalent with ChatGPT's newer coding models, particularly when you start cranking up the reasoning effort. Spend a few minutes browsing the Codex subreddit and you'll find users complaining about the exact same behaviors: creating unnecessary helper functions, adding defensive checks for hypothetical edge cases, introducing new abstractions where simpler code would have worked, and generally expanding the scope of a task beyond what was requested. The complaints are especially frequent around GPT-5.6 Sol, which is the newest and most powerful model in OpenAI's current lineup. OpenAI, as with other AI labs, has constantly been directing its efforts toward making its models more capable of long-running autonomous work. With every generation, these models are getting better at exploring a codebase on their own, breaking larger tasks into smaller steps, spotting adjacent issues, and continuing to work without needing you to guide every decision. That's obviously useful when you're handing Codex a complicated project and want it to take as much initiative as possible. The problem is that the same behavior can become excessive when the task itself is simple. Instead of treating your request as a narrow change, Codex can start looking for other things to improve along the way, which is how a small fix can quickly turn into a much larger piece of work. This is why I add one boundary to every Codex prompt Sometimes, less really is more With LLMs, you can essentially mold their behavior around the way you work simply by being more explicit about what you do and don't want. So, rather than hoping Codex would eventually learn to show more restraint on its own, I started adding one extra line to the end of my prompts: Make the smallest change that fully solves the task. Do not add abstractions, fallbacks, defensive guards, refactors, or features unless they are strictly required. This line essentially gives Codex a strict boundary without hampering how it thinks through my requests. I'm not telling it how to solve the problem, which files to edit, or what exact approach it needs to take. All I'm doing is instructing the model that doing more isn't necessarily the same thing as doing a better job. I'm simply making it clear that the goal isn't to produce the most elaborate solution it can think of. The goal is to solve the task with as little unnecessary work as possible. That distinction has made a surprisingly noticeable difference. Codex still has the freedom to inspect the codebase, reason through the problem, and make whatever changes are genuinely necessary. However, it seems far less eager to turn every request into an opportunity to clean up the surrounding code or account for scenarios that may never actually happen. I also prefer this approach to loading my prompts with a long list of rules. Part of the appeal of using a capable coding agent is that I don't want to micromanage every step it takes. This one line lets Codex keep doing the difficult reasoning for me while giving it a much clearer definition of what I actually want. Sometimes, the best prompt tells Codex to do less As coding agents become more capable and autonomous, getting better results isn't always about telling them more and is simply about being clearer about what they shouldn't do. The prompt I included above does exactly that.