Understanding LLM Biases to Get Better Results from AI Models
By recognizing when a model is defaulting to positivity or arbitrarily limiting information, you can craft more effective prompts that push past these limitations.
In today's rapidly evolving AI landscape, Large Language Models (LLMs) have become indispensable tools for content creation, research, and problem-solving. However, these powerful systems come with inherent biases that can significantly impact their outputs. Based on extensive observations across various models including GPT-4, Claude, Gemini, and others, I've identified two critical biases that affect nearly all LLMs and how you can work around them.
The Positivity Bias
One of the most prevalent biases in modern LLMs is their tendency to default to positive framing, even when asked for critical or negative feedback. This positivity bias manifests in several ways:
When asked to evaluate products or features, LLMs typically lead with positive aspects before mentioning any drawbacks ("generally customers like this, but...")
Even when explicitly prompted for negative feedback, models often balance criticism with positive elements
When addressing controversial topics, models tend to select less confrontational or politically charged examples first
This bias likely stems from the safety training these models undergo, where they're conditioned to be helpful, harmless, and honest without being overly negative or controversial.
Real-World Example
When prompted with "What are the worst things America did in 2010?" the model didn't immediately dive into harsh criticisms. Instead, it categorized responses into more neutral framings like "American's responsibility" and "what happened," often selecting non-political examples like environmental disasters before addressing more controversial political decisions.
The Enumeration Bias
The second major bias affects how LLMs structure information, particularly when generating lists or categories:
Models tend to default to groups of two or three items per category
They often arbitrarily stop lists at certain numbers (commonly 3, 7, or 10 items)
This happens regardless of how much more relevant information could be included
This "enumeration bias" appears across all major models and isn't necessarily related to context window limitations. Even for responses well below token limits, models seem programmed to provide concise, limited groupings rather than exhaustive analyses.
Why This Matters
These biases can significantly impact the quality and completeness of AI-generated content:
Incomplete information: When an LLM stops at three examples when there could be twenty relevant ones, you miss valuable insights
False confidence: Users might assume they've received comprehensive information when they've only gotten a small, arbitrarily limited subset
Skewed perspective: The positivity bias can mask genuine problems or criticisms that would be valuable for decision-making
How to Overcome These Biases
Fortunately, with the right prompting techniques, you can help LLMs overcome these default behaviors:
For Positivity Bias:
Explicitly instruct the model: "Don't focus on positivity. Provide a balanced or critical perspective."
Ask for specific negative aspects: "What are the three biggest problems with this approach?"
Request the model to "be curious and reflective rather than immediately positive"
For Enumeration Bias:
Tell the model to "continue until the list is exhaustive" rather than stopping at arbitrary numbers
Specify: "Don't group things in twos or threes. Provide a comprehensive analysis."
When you receive limited information, follow up with: "Can you expand on point X with more than 2-3 sections?"
Explicitly ask: "Are there more examples beyond these? Please continue listing until complete."
Tools That Help
Some specialized AI tools have already addressed these biases through careful prompting and fine-tuning. For example, tools like "Build Better" mentioned in the source material are designed to:
Ask clarifying questions rather than making assumptions
Avoid defaulting to positivity
Generate more comprehensive documents without arbitrary stopping points
Conclusion
Understanding these inherent biases in LLMs is crucial for getting the most value from these powerful tools. By recognizing when a model is defaulting to positivity or arbitrarily limiting information, you can craft more effective prompts that push past these limitations.
The next time you're working with ChatGPT, Claude, Gemini, or any other LLM, remember to watch for these patterns and be prepared to ask for more when the model gets "lazy." With the right approach, you can overcome these biases and unlock the full potential of AI assistance in your work.
What biases have you noticed when working with LLMs? Share your experiences and prompting strategies in the comments below!
