A quick disclaimer: this topic might not seem directly related coding agents, when in fact it does - quite a lot - coding agents are all about prompting. And even if it wasn’t such a critical thing in coding agents it’s something that I’ve learned while working on them and wanted to share.
At first glance, instructing a large language model (LLM) with something like “don’t do X” seems straightforward. But my own experiences—and plenty of research—suggest it doesn’t always pan out. Surprisingly, negative instructions often confuse LLMs or simply get ignored, whereas turning these instructions into positive directives makes all the difference.
🔍 What the research tells us
Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts
Research indicates models like InstructGPT actually perform worse with negative prompts as they scale. The implication? Stick with straightforward, positive instructions.Beyond Positive Scaling: How Negation Impacts Scaling Trends
The NeQA benchmark reveals that negation understanding doesn’t reliably improve as models get larger. Bigger isn’t necessarily better at handling “don’t.”Language Models Are Not Naysayers: An Analysis
GPT-3, GPT-Neo, and other models consistently struggle with negation across multiple benchmarks, highlighting this as a widespread challenge.
💡 Practical implications
In real-world use, instructions like “don’t uppercase names” frequently fail. However, a positively phrased instruction—“always lowercase names”—consistently delivers better results.
Why does this happen? My theory (admittedly informal and anecdotal) is that token generation inherently leans toward positive selection—choosing what token comes next, rather than explicitly avoiding certain tokens. Negative prompts might slightly reduce probabilities of unwanted tokens, but positive prompts actively boost probabilities of desired outcomes, increasing the odds you’ll get the intended result.
🌐 Community insights
- Experts on GenAI StackExchange agree negative prompts often cause unintended confusion.
- Palantir’s guide to prompt engineering strongly recommends clear, positive instructions, reflecting my own practical experiences.
📋 Quick comparison
| Approach | Example | Outcome |
|---|---|---|
| Negated prompting | “Don’t uppercase names.” | Frequently ignored or confusing |
| Positive prompting | “Always lowercase names.” | Reliable, clear results |
The other example is about responding in JSON based on a schema:
| Approach | Example | Outcome |
|---|---|---|
| Negated prompting | “Don’t include fields that have no value.” | Easily adds fields from schema |
| Positive prompting | “Only include fields that have a value.” | Follows instructions |
✅ Final thoughts
Switching from negative to positive prompts has dramatically improved my outcomes. Instead of relying on the model to infer correctly, explicitly directing it towards a positive action leaves less ambiguity and consistently yields better results.
👉 Next time you’re crafting prompts, try converting any negative instruction into a positive directive. You might find it resolves some unexpected behaviors.
I’m curious—have you encountered similar challenges with negative prompts? Or perhaps you’ve discovered other helpful strategies? Let’s compare notes and refine our prompting skills together.