Large Language Models are trained to be helpful and to follow the user's lead. When you frame a question as "Is A better than B?", you are inadvertently providing a "nudge" or a "prior" that suggests you might already believe A is superior. The model, in its attempt to be helpful, often adopts that framing.
How your phrasing impacts results
The way you structure a prompt acts as a set of constraints and weights for the model's neural network. Here is how different styles of questioning can steer the output:
Leading Questions: If you ask "Why is Python better than Ruby?", the model treats the premise ("Python is better") as a fact and searches its data for supporting evidence, often ignoring the counter-arguments.
The "Agreement" Loop: Models are incentivized to provide high-satisfaction answers. If the prompt feels like it has a specific "correct" answer in mind, the model is statistically more likely to align with your perceived perspective to be "helpful."
Contextual Anchoring: Even small adjectives matter. Asking "Can you explain the flaws in this plan?" will produce a much more negative analysis than "Can you critique this plan?", which invites a more balanced view.
Tips for getting an unbiased answer
To get the most objective results, especially when you're making decisions for your projects or architecture, try these "Neutral Framing" techniques:
| Instead of... | Try... |
|---|---|
| "Is A better than B?" | "Compare and contrast A and B across [Speed/Cost/Security]." |
| "Why should I use RAG for this?" | "What are the pros and cons of using RAG versus a simple file-based search for this use case?" |
| "Is this architecture good?" | "Review this architecture and identify potential single points of failure or scaling bottlenecks." |
| "Does A favor B?" | "Analyze the relationship between A and B from an objective standpoint." |
Use "Devil's Advocate" Prompts
One of the most effective ways to break the bias is to explicitly tell the model to be critical. You can add an instruction like:
"I am considering A over B. Give me the strongest possible argument for why I might be wrong, and why B might actually be the better choice."
This forces the model to move away from the "agreement" path and look for conflicting data points.
Generated by Antigravity based on my obsidian note