Why You Should Understand Machine Learning and GPT Technology Before You Start Prompting Your AI

Most of us met large language models through a chat box. You type something, it answers, and it feels like talking to a very well-read colleague. That feeling is useful, and it is also the source of most of the frustration developers run into later. The model is not a colleague. It is a statistical system with specific habits, and once you know those habits, your prompts get better, your agents get more predictable, and your token bill gets smaller.

You don't need a history lesson, but three moments explain most of what follows.

In 2017, Google researchers published Attention Is All You Need, introducing the transformer architecture. Its key idea, attention, lets a model weigh every token in the input against every other token, rather than reading strictly left to right. Then OpenAI's GPT line (2018 onward) showed that if you train a transformer on a huge amount of text with one simple goal, predicting the next token, it picks up grammar, facts, code, and reasoning patterns as a side effect. Finally, ChatGPT in late 2022 added instruction tuning and reinforcement learning from human feedback, which made these models feel like assistants rather than autocomplete engines.

The takeaway: underneath the polish, it is still next-token prediction. Everything below follows from that.

An LLM does not look things up. It has no database of facts it consults. During training, its weights were adjusted so that, given a sequence of tokens, it assigns high probability to plausible next tokens. When you prompt it, it generates one token at a time, each chosen based on your prompt plus everything it has generated so far.

A hallucination is what happens when the most plausible continuation is not a true one. A few common causes:

Hallucination is not a bug that will be patched out in one release. It is a natural property of the mechanism, which can be reduced but not eliminated. Treat output as a draft to verify, not a source to trust.

Here is a thought worth exploring. I want to be upfront that this is an informed hunch, not an established finding, so treat it as a hypothesis to test in your own work.

A lot of developers load as much as they can into the prompt: whole files, long documentation dumps, old conversation history, pasted logs. Call it context stuffing. (The more disciplined version of the same activity is often called context engineering: deliberately choosing what the model sees.)

LLMs are trained on an enormous mix of natural languages, programming languages, markup formats, and jargon. That breadth is a strength, but it also means many tokens are ambiguous. The word "table" could be furniture, a database table, or an HTML element. "Class" could be a school class, a CSS class, or an OOP class. An identifier like order could be a purchase, a sort direction, or an instruction. Variable names in one language can resemble words in another. Mixed-language comments, English prose wrapped around Python, JSON, and shell output all sit in one window.

The more heterogeneous material you pour in, the more room there is for the model to latch onto the wrong interpretation of a token or to blend patterns from different domains. Add stale or contradictory snippets and the model has to guess which one you meant. A guess dressed as a confident answer is exactly what a hallucination looks like.

Research gives this hunch some partial support: studies on long-context behavior (for example the "lost in the middle" findings) show that models use information unevenly depending on where it sits in the context, and that irrelevant material can degrade answers. Whether linguistic breadth specifically is a driver is something I can't claim, but the practical advice holds either way: give the model less, cleaner, more relevant context, and say what the terms mean.

In a chat, a bad answer costs you a retry. In an agentic app, where the model plans, calls tools, reads results, and loops, a bad step can trigger real actions. Knowing how the model works explains most of the surprises.

Why agents produce undesired output or do things you didn't ask for:

What helps: state the goal and the boundaries explicitly (including what not to do), give each tool a narrow, distinct purpose and a precise description, grant the minimum permissions needed, require confirmation before destructive actions, keep the original task restated near the end of the context, and validate tool outputs before they re-enter the loop.

Tokens are what you pay for, and also what fills your context window. Understanding the mechanics saves them in several ways:

Prompting is not magic words. It is communicating with a next-token predictor that has no ground truth beyond its training and your context window. Once you internalize that, a lot of "mysterious" behavior stops being mysterious: hallucinations are plausible-sounding gap-filling, agents overreach because you left room to, and your token bill is mostly a record of how much unnecessary material you sent.

You don't need a PhD. Reading about how transformers work, how tokenization splits text, how context windows behave, and how sampling works is a few hours of effort that will improve every prompt you write afterward.

If you've seen mixed-language or messy context cause strange outputs, I'd like to hear about it in the comments. This is exactly the kind of hypothesis that gets better with more real-world examples.