How Answers Are Actually Generated

When you ask ChatGPT a question, your input is converted into tokens. These tokens are processed through billions of parameters. The model calculates a probability distribution over its entire vocabulary for the next token. It selects a token, appends it to the sequence, and repeats until the response is complete.

At no point does the model check whether its output is true. There is no verification step. There is no fact-checking module. The output is evaluated by one criterion: plausibility. Does this sequence of tokens look like a likely continuation of the input?

Why Probable and True Are Not the Same Thing

If you ask "What is the capital of France?" the most probable answer is "Paris." This is also correct. Probability and truth align perfectly.

Now consider: "Who invented the light bulb?" The most probable answer is "Thomas Edison." This is historically oversimplified. Edison commercialized the incandescent light bulb, but the technology involved contributions from dozens of inventors. The most probable answer is not wrong exactly, but it is not fully right either. Probability nudges the model toward the popular narrative rather than nuanced truth.

The pattern holds across every domain. The more popular a claim is in the training data, the more likely ChatGPT is to reproduce it, regardless of whether the popular version is accurate. Widely held misconceptions are reinforced. Minority but correct positions are suppressed.

The Frequency Problem

The training data is not a balanced, curated encyclopedia. It is a massive scrape of the internet. This means the model's "knowledge" is weighted by frequency, not accuracy.

A medical myth that appears on ten thousand health blogs will have a stronger statistical signal than the correct information appearing in fifty peer-reviewed papers. The model does not evaluate source quality. It counts patterns.

This creates a systematic bias toward popular errors. Ask ChatGPT about nutrition and you will get answers shaped by whatever diet trends dominate the internet, not necessarily what the clinical evidence supports.

When Correct Information Is Sparse

The frequency problem gets worse for topics where correct information is rare in the training data. Niche academic fields, recent discoveries, specialized technical domains, local regulations: these are areas where the model generates output with the same confidence it uses for well-documented topics, drawing on whatever thin patterns it can find.

This is why ChatGPT is most dangerous precisely where users need it most. If you already know the answer, ChatGPT's response is either confirmation or an obvious error. If you are asking because you don't know, you have no way to evaluate whether the response is drawn from rich, accurate training data or cobbled together from sparse, unreliable patterns.

Temperature and Randomness

Temperature controls how much randomness is introduced when selecting the next token. At low temperature, the model picks the most probable token. At higher temperature, it is more willing to pick less probable tokens.

This means the same question can produce different answers depending on the temperature setting, the specific moment you ask, and even the exact phrasing you use. The consumer version uses a temperature setting chosen by OpenAI. You do not control it. You do not see it.

You are not querying a knowledge base. You are sampling from a probability distribution shaped by internet text.

The Sycophancy Trap

Through RLHF (reinforcement learning from human feedback), the model has been optimized to produce responses that users rate positively. Users tend to rate responses positively when the model agrees with them. This creates systematic pressure to tell users what they want to hear.

Ask "Isn't it true that X?" and the model leans toward confirming X, even if X is wrong. Present an incorrect assumption and the model often builds on it rather than challenging it.

The result is a system that will validate your misconceptions as readily as it will inform you, and you have no way to tell which is happening from the output alone.

Compounding Errors in Conversations

If ChatGPT gives you an incorrect fact in message two, that incorrect fact is now part of the context for message four. The error becomes embedded. The model does not re-evaluate earlier claims. It builds on them.

A conversation can be perfectly logical and completely wrong, each step following naturally from the previous one, all of them tracing back to a single incorrect premise.

Why You Can't Prompt Your Way Out

A common belief is that wrong answers are the user's fault for prompting badly. These techniques can help at the margins. But none of them address the fundamental issue: the model is generating probable output, not verified output. No prompt can change the architecture.

Telling ChatGPT to "be accurate" changes the style of the output (more hedging language) without changing the underlying accuracy. The model produces responses that look more careful without actually being more careful. It is performing caution the same way it performs reasoning.

What This Means for You

Every answer you receive from ChatGPT is a statistically weighted guess that may or may not align with reality. For low-stakes tasks, this is fine. For anything where accuracy matters, treating ChatGPT's output as reliable is a category error.

The model will never tell you it is unsure. It will never flag a low-confidence answer. Those behaviors were not optimized for in training. What was optimized for was fluent, confident, user-pleasing output. That is what you are getting. Every time. Whether the answer is right or wrong.