How AI generates answers: the mental model that makes prompting click

Most explanations of how AI works land somewhere between "it's like a person" (which is wrong in a way that gets you burned) and "it predicts the next word" (which is technically correct but unhelpfully vague). This article is the middle ground: a working mental model that takes ten minutes to absorb and immediately changes how you prompt.

You do not need any math. You do not need to know what a transformer is. You need one idea, and four consequences.

The one idea

When you send a message to ChatGPT, Claude, or any modern AI assistant, it is doing something like this:

Given the training patterns and the conversation you just sent me, what continuation is most plausible?

It then generates that sentence one tiny chunk at a time — a few characters or a word — and feeds each chunk back into itself to predict the next one. It keeps going until the response reaches a stop condition.

That is the core loop. There is no inner search for "the answer," no consultation of a database unless a tool is connected, and no reasoning module in the human sense. There is a giant statistical pattern-matcher producing a continuation that looks like the kind of thing a knowledgeable person would write in this situation.

It feels intelligent because the patterns it learned during training came from human-written text: grammar, facts, jokes, code, contracts, recipes, arguments, apologies, the difference between a polite reply and a sarcastic one. So when you ask it something, the most plausible continuation is often the right answer. Often. Not always.

Now four consequences fall out of this. Each one makes a whole category of "prompt engineering tips" obvious.

Consequence 1: It does not verify what is true

The model is not optimized to be correct. It is optimized to produce plausible text. Most of the time these overlap, because the training data contained correct information about common topics. But when you ask a question where the model has not seen reliable information — a niche legal case, a specific recent event, an obscure quote — it does not stop. It generates a plausible-sounding answer anyway.

This is why hallucinations happen. Not because the model is broken, but because it is doing exactly what it was built to do: produce a plausible continuation. Plausibility and truth are correlated, not identical.

The implication: any specific factual claim from an AI — a name, a date, a number, a citation — needs a verification habit. The model may express uncertainty, but you should not rely on that as the safety system. Build the check into your workflow.

Consequence 2: Context is everything

If the model is predicting the most plausible next sentence given your prompt, then the prompt is the input that determines what it produces. Add more relevant context — who you are, what you have already tried, what you want — and the model has a richer pattern to match against.

This is why "more context" is the single most reliable way to improve any answer.

"Write me an email" produces the kind of generic email someone with no information would write.

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"Write me an email to a long-term client we missed a deadline with, in a direct but warm tone, under 100 words, ending with a clear next step" produces something close to what you actually want.

The model did not get smarter between the two. It got a richer prompt, and that prompt narrowed the space of "plausible continuations" to a much more useful zone.

When experienced AI users tell you that prompting is "just being more specific," this is what they mean. It is not a trick — it is the model doing its single job, more accurately, because you gave it more to work with.

Consequence 3: The conversation is part of the context

Every modern AI assistant keeps the whole conversation in its working memory (called the context window). When you reply with "make the second paragraph shorter," the model is not starting from scratch. It is producing the most plausible next response given everything in the conversation so far — the original email, the draft it produced, your critique, and now a request to shorten the second paragraph.

This is why the second draft is almost always better than the first one. The model has more to work with: the original prompt, its own attempt, and your reaction.

It also has implications:

• Long, useful conversations are a feature, not a bug. Stay in one thread instead of starting fresh.
• Pasting your critique back in beats rewriting the prompt. The model needs less context if it already has it.
• Once the conversation gets very long, the model starts losing the early parts. Top-tier products in 2026 can reach very large context windows (a million tokens or more in some cases), but the plan you actually pay for is often much smaller, and quality drops as the window fills up. For genuinely long projects, summarize and start fresh occasionally.

Consequence 4: How you frame the question shapes what comes out

Because the model produces a plausible continuation, the kind of continuation it produces depends heavily on the tone and shape of your prompt.

Ask a question in a casual, vague way, and you get a casual, vague answer. Ask the same question in a structured, specific way, and you get something more rigorous.

Tell the model to decompose the problem before giving its final answer, and it will generate intermediate analysis that often makes the final answer better. It is not magic. It is steering more of the response toward decomposition, checks, and comparison before the conclusion, which can produce more reliable answers on hard problems.

Tell the model to play a particular role — "you are a senior tax accountant in Estonia" — and it will draw on the patterns of how such a person would respond. The model is not actually becoming a tax accountant. It is matching the style and content density of expert-tax-accountant text.

Tell the model to argue against your idea, and it will produce the most plausible counterargument. Tell it to play the skeptical customer, and it will produce skepticism. Tell it to be brief, and it will compress.

Every "prompt engineering pattern" you have ever seen is a variation of this consequence. Role prompting, worked examples, critique prompts, "argue against," "be concise," "match this tone" — they all work because they steer the model's prediction toward a different kind of plausible continuation.

For work tasks, the safest prompt is rarely the cleverest wording. It is a clear package of context, constraints, source material, output format, and explicit uncertainty handling.

Putting it together

Hold those four consequences in mind the next time you send a prompt:

1. The model does not verify what is true; it produces what is plausible.
2. More context narrows the space of plausible answers toward what you actually want.
3. The whole conversation is the context, so use the back-and-forth.
4. The shape and tone of your prompt shape the shape and tone of the answer.

Notice that all four come down to the same observation: the prompt is the bridge between the model's training patterns and the output you actually want. The shorter you can make that bridge — without losing information — the better.

That is the entire game.

The practical prompt package

For real work, a good prompt usually contains six parts:

| Part | What it gives the model | | --- | --- | | Role or perspective | The type of answer pattern to use | | Context | The situation, audience, and constraints | | Source material | The text, data, or examples to work from | | Task | The specific job to perform | | Output format | The shape of the answer | | Uncertainty rule | What to mark, verify, or refuse |

This is why vague prompts produce vague answers. They leave too many plausible continuations open. The companion exercise linked from this article helps you turn a weak prompt into this six-part package.

A small test

Take a prompt you sent recently that produced a mediocre answer. Look at it. Then ask yourself:

• Did I give the model enough context about who I am and what I want?
• Did I include any constraints — length, tone, format?
• Did I show it an example of what good output looks like?
• Did I ask it to decompose, push back, or mark uncertainty?

You will almost always find at least one of those missing. Add it and send the prompt again. The answer will usually improve because you narrowed the space of plausible continuations toward the work you actually need.