Mitigating LLM Biases: Why Large Language Models Default to Positivity & ‘2-or-3’ Answers—and How to Push Past Them

Most people assume Large Language Models (LLMs) like ChatGPT, Claude, or Gemini can generate perfectly balanced, thorough responses every single time. In reality, there are two big (and surprisingly universal) biases that tend to show up again and again, no matter which model you’re using:

1. Positivity Bias
2. The “2-or-3” Grouping Bias

Below, I’ll break down exactly what these biases look like, why they happen, and how to mitigate them so you can get the most out of any LLM you’re working with.

1. The Positivity Bias

LLMs generally default to a rose-colored perspective. Even if you ask for something critical or negative, the model often tacks on “but here’s the bright side!” Or it’ll soften critiques and go easy on calling out explicit downsides.

Why It Happens

• Training Data: Many LLMs are trained on datasets that favor polite or “helpful” language. They interpret negativity as less “helpful,” so they pivot to positivity instead.
• Content Moderation: Built-in guardrails can push the model to steer clear of overtly negative or controversial statements.

When It’s Problematic

• Critical Feedback: If you’re gathering user feedback, you might only hear the LLM’s softened version of a real complaint.
• Risk Assessment: You can’t fully understand potential pitfalls if the model always tries to wrap everything in a “silver lining.”

How to Counteract It

• Prompt Explicitly for Negatives: Say, “List ALL potential risks or downsides. Do not add optimistic language.”
• Ask for Clarification: Prompt the model to ask you questions if it’s unclear rather than glossing over negativity.
• Emphasize Candor: Use instructions like “Be candid, direct, and do not soften the potential criticisms or concerns.”

2. The “2-or-3” Grouping Bias

Ever notice how LLMs seem to love offering exactly two or three examples, bullet points, or potential solutions? This happens even when you haven’t told it to be concise. It’ll spontaneously limit itself to just a couple of items—whether it’s listing product features, user feedback, or steps in a process.

Why It Happens

• Token & Attention Limits: LLMs have inherent token constraints (often 2k–4k tokens for standard tiers), and they’re trained to produce “neat” chunks of text. Two or three examples are short, easy, and typically “good enough.”
• Overlearning Patterns: Large swaths of training data show that short, bite-sized lists are standard. The model picks up these patterns and reproduces them.

When It’s Problematic

• Incomplete Feedback: If you ask, “Show me all the user complaints in our last 50 reviews,” the LLM might stop after two or three issues—even if there are 15 legit points.
• Lost Insights: Important details or minor issues can slip through because the LLM lumps data into just a couple bullet points and calls it a day.

How to Counteract It

• Prompt for Exhaustive Answers: Use language like, “Continue listing until you have nothing left” or “Provide every single relevant detail—do not group them artificially.”
• Ask Follow-Up Questions: “Thanks for these three. Are there more?” or “Expand on #3 with at least 10 more examples.”
• Use Tools with Built-In Prompting: Platforms like BuildBetter (and others) have internal prompts that override these default biases. They can automatically push the LLM to ask clarifying questions and avoid truncating answers.

Bonus Bias: The “Lazy” Default

LLMs rarely ask clarifying questions on their own. They’ll guess or fill in gaps rather than confirm what you actually meant. This “lazy” default can be especially frustrating if you’re asking the model to draft detailed documents (e.g., PRDs, user stories) because it won’t pause to say, “Wait, what kind of feature are we building exactly?”

Pro Tip: Force the model to ask for context. For example:
“Before you produce the final answer, ask at least three clarifying questions if the requested scope is not explicit.”

Putting This into Practice

Let’s say you want to generate a Product Requirements Document (PRD) for a new search function. If you simply say, “Hey, write a PRD for a search bar,” the LLM might:

• Only highlight two or three requirements.
• Offer an overly rosy product vision with no potential drawbacks.
• Skip clarifying questions like “What kind of search are we building exactly? Keyword, semantic, or something else?”

Instead, prompt it like this:

“Write a PRD for a new semantic search feature. Break down all user needs—list every single one without grouping them artificially or limiting them to two or three. Include realistic concerns or drawbacks. If you’re missing any context, ask me clarifying questions first.”

You’ll instantly see more depth and nuance in the final output.

Key Takeaways

1. LLMs skew positive: They’ll usually wrap up negativity in a positive spin, so push them to be brutally honest when that’s what you need.
2. They love small lists: Don’t let them stop at two or three solutions or issues. Prompt for exhaustive sets.
3. They won’t ask if they’re confused: Instruct them to request clarifying details before generating final answers.

Bottom line: These biases exist in every major LLM—ChatGPT, Claude, Bard, Grok, you name it. They’re not deal-breakers, but you’ll need to tweak your prompts or use tools that handle advanced prompting under the hood. Once you know how to steer them, LLMs become way more powerful, thorough, and (yes) honest.

Happy prompting!