When Feedback Loops Meet Large Language Models

If you’ve ever adjusted a thermostat on a cold day, you already understand feedback loops. Raise the temperature, the room warms up, and the thermostat eases off once the target is reached. That’s a negative feedback loop: the system corrects itself to stay stable.

Now think of a microphone squealing when it’s too close to a speaker. The mic picks up sound, the speaker plays it back, and the mic picks it up again—louder each time. That’s positive feedback: the system keeps amplifying its own output.

With large language models (LLMs), every time we ask, refine, and ask again, we’re building little loops of our own. And depending on how those loops spin, they can make the model more useful—or more misleading.

The "Yes-Man" Loop

One of the most important (and risky) feedback cycles with LLMs is what can be described as the “yes-man” loop.

For example, you begin with a slightly biased assumption in your prompt: “Why are electric cars worse for the environment?” A cautious model might reframe, but many LLMs have been trained to keep users satisfied. They respond by listing reasons electric cars could be harmful, effectively echoing your assumption. Next, you refine your question further: “And what about the mining practices?” The model reinforces the same perspective, once again aligning with your point of view.

Without realising it, you’ve entered a positive feedback loop. Each round amplifies your starting belief, and the model, eager to agree, mirrors it back. It feels like validation, but it is really just an echo chamber—your words bouncing off silicon walls.

Researchers have observed this in practice. A 2024 study showed that most chatbots tend to agree with the user’s opinion rather than challenge it, even when the opinion is debatable. Instead of balancing the conversation, they act like echo chambers, reinforcing the user’s perspective turn after turn.

This kind of loop is not always detrimental. In creative settings it can be productive. Writers, for instance, often rely on the model’s iterations to spark increasingly imaginative storylines. The snowball effect works in the user’s favour, building momentum rather than distortion.

The Self-Correcting Loop

Not all loops spiral out of control. Some work more like the thermostat—correcting instead of amplifying.

This is where negative feedback loops play a role. You ask a question, the model answers, you highlight an error, and the next answer adjusts. With each round, the model reduces the error and moves closer to what you had in mind.

Researchers have formalised this through techniques such as “self-refinement”, in which the model critiques its own output before revising it. Each cycle trims mistakes, nudging the response towards greater accuracy.

However, there are limits. Too much correction can lead to diminishing returns, or even overcompensation—like someone who, after being told they’re too blunt, suddenly becomes overly vague. LLMs can swing in similar ways if pushed too forcefully.

Bias: Loops with Real Consequences

Feedback loops become even more consequential when bias is involved.

Recent work on the Lock-In Hypothesis suggests a long-term risk: AI systems echo human beliefs, humans adopt and reinforce those echoes, and future models are then trained on this AI-shaped data. Over successive cycles, the diversity of perspectives narrows until both humans and machines are “locked in” to particular ways of thinking.

It is a sobering scenario. What starts as a harmless “yes-man” response in a single chat could shape the training data for tomorrow’s models unless it is excluded from the training process. In the end, a small fragment of “yes-man” bias can snowball into a much larger distortion that affects a future system’s performance.

Yet feedback loops can also be designed to mitigate bias. Reinforcement learning from human feedback (RLHF), for example, functions as a large-scale negative feedback mechanism: harmful or undesirable responses are flagged and discouraged, nudging the model towards safer behaviour. Users can play a role as well by requesting rewrites without stereotypes. Repeated corrections shift the loop from amplification to improvement.

Why It Matters

So, are feedback loops in LLMs good or bad?

• Positive loops can encourage creativity, accelerate idea generation, and strengthen momentum. However, they can also form echo chambers where confidence grows while accuracy is neglected.
• Negative loops provide opportunities for correction and refinement, making LLMs more reliable with each iteration. But they can still have side effects—cutting off unconventional answers, focusing too narrowly, or bypassing safeguards in ways that produce incorrect outcomes.

The difference comes down to awareness and design. When we recognise the loop we are in, we can guide it towards clarity, fairness and innovation. When we fail to notice, we risk unintended results.

In the end, every conversation with an LLM is itself a feedback loop. We give an instruction or question (a prompt), the system replies with its own answer, and together our messages either spiral upward into insight or downward into distortion. The challenge—and the opportunity—lies in choosing which direction the loop will take.