Why Do Large Language Models (LLMS) Answer Differently to the Same Question?
It’s a question that might surprise you: why does an AI model, when asked the same thing twice, give slightly different answers? The short answer? These models are designed to mimic human intelligence. And humans, as you know, don’t repeat themselves verbatim.
Let’s start with a simple example
Imagine you’re asking the most knowledgeable professor in a highly specialized field the same question twice, like:
“What causes the aurora borealis?”
Even if the professor is an expert, they’re unlikely to repeat their answer word-for-word. The core ideas will remain the same, but they might choose different examples, reorder their explanation, or emphasize certain points depending on subtle factors — like how they’re feeling or how they interpret your interest at that moment.
Similarly, LLMs are designed to generate responses based on patterns from vast training data, much like the professor draws on years of expertise. The variability is a feature, not a bug—it reflects the diversity of ways the model can communicate knowledge.
Why Variability is Not a Problem
- Humans Already Accept This as Normal
- In the real world, even experts rarely answer the same question the same way twice. This doesn’t reduce trust in their expertise; it’s just part of human communication.
- What’s important is consistency of meaning, not consistency of phrasing. LLMs are built to prioritize delivering the right ideas, even if the wording shifts slightly.
- Search Experience Sets Expectations
- People often expect consistent, static answers because of familiarity with search engines, which retrieve fixed pieces of content. This gives a programmatic impression: the same input produces the same output.
- However, LLMs as standalone are generative, not retrieval-based. They synthesize responses in real-time, adapting to context and the natural variability of language.
- Not a Programmatic System
- Unlike traditional systems with fixed outputs (like a calculator), LLMs mimic the way humans think and communicate. This fluidity makes them capable of addressing nuanced, creative, and evolving queries.
Why This Approach is Better
- Richer Communication: If an expert professor could only repeat a fixed script, their teaching would feel robotic and limited. Similarly, an LLM’s variability allows for richer, more dynamic, and adaptable interactions.
- Focus on Outcomes: What really matters is whether the model delivers accurate, meaningful, and actionable responses - not whether the phrasing is identical every time.
Reassurance for Users that have to adapt
We understand that some users might expect identical answers, especially if they associate LLMs with search engines or deterministic programs. However, this variability reflects a strength: the ability to generate contextually relevant and diverse insights, much like an expert human.
If absolute consistency is crucial for your use case—such as regulatory compliance or training—we can adjust the model’s behavior (e.g., using a lower temperature setting, adding memory) to meet those needs. But for most scenarios, the flexibility mirrors how people operate today, ensuring the interaction feels natural and effective.
Example from ChatGPT on 19.10.2024
When you ask an AI the same question more than once, the responses often share a common core but differ in tone, detail, and structure. Below, we’ve captured two answers generated by the same AI to the question: “What is GPT?” While both responses cover foundational aspects like the Transformer architecture and its pretraining on large datasets, they also diverge in their level of detail, technical depth, and how they present applications.
A concise and high-level explanation of GPT, focusing on its function as a natural language model based on the Transformer architecture. It briefly outlines its capabilities in understanding and generating coherent text, with emphasis on its training process.
But let's ask the exact same question again:
A detailed and structured explanation of GPT, emphasizing its technical aspects (self-attention, pretraining, and fine-tuning) and scalability (e.g., GPT-3, GPT-4). It highlights diverse applications and features, providing examples of its versatility in real-world use cases.
When we compare the two answers provided by the AI to the same question - “What is GPT?” - the similarities are clear and consistent, but the differences provide insight into how the model tailors its responses: Both responses consistently describe GPT as a Transformer-based language model pre-trained on large datasets for tasks like text generation and understanding. One response provides a concise overview, while the other dives into detailed technical aspects, structured explanations, and specific applications. These differences illustrate how the same model, with the same underlying capabilities, can shift its approach depending on subtle contextual factors.
Closing Thoughts: The Beauty of Adaptive Intelligence
The variability in AI responses isn’t a flaw—it’s a reflection of progress. Much like a seasoned expert adjusts their explanation based on the audience, Large Language Models adapt to subtle cues, providing flexibility and nuance in their communication. This adaptability allows them to engage meaningfully, whether offering concise overviews or detailed technical insights.
Incorporating document retrieval into the process (RAG) adds an extra layer of reliability, ensuring factual alignment while preserving the dynamic, conversational nature of the responses. Together, these systems represent a shift away from rigid, programmatic answers toward a more human-like intelligence.
As we move forward, the key isn’t just in creating systems that answer questions - but in building systems that understand. The ability to adapt, contextualize, and evolve is what makes AI more than a tool; it makes it a partner in communication, innovation, and discovery. And just like with any great conversation, it’s the dynamic exchange that truly brings the interaction to life.
