How LLMs Work

How Do LLMs Work ChatGPT can draft an email in ten seconds. Claude can spot which bug you missed twice. Gemini can turn your fifty-page report into something you’ll actually finish reading.

Different tools, same engine driving underneath all three: a large language model.

Explaining that engine goes two bad directions. Too much transformer math and it just buries the lede, while staying vague makes the explanation just a list of buzzwords without a reason.

I’ll aim for the sweet spot: what an LLM is, how it’s trained, where it’s already present in products you use, and how they fail in ways that matter.

We’ll also address two comparisons that confuse nearly everyone at some point: LLM versus generative AI, and LLM versus NLP.Stick with it, and “Large Language Models Explained” stops being a search you need to run twice.

What Are Large Language Models

Large language models are neural networks trained on huge amounts of text with one specific purpose in mind: guess which word comes next.

Straightforward enough, at least on the surface. Do the job across billions of parameters and trillions of words and the skill begins generalizing on ways you weren’t expecting it going in.

Everything from writing to reasoning to code stems from the same repeated exercise.None of that is from understanding, not how humans do it.

An LLM doesn’t remember your address, but it does recognize patterns in far more text than you could ever read in ten lifetimes.

This disconnect between pattern matching and understanding is the precise reason the limitations in the latter part actually matter.

How Large Language Models Work:

Architecture and TrainingMost large language models are based more or less on a transformer architecture, and the piece that’s worth knowing about is the self-attention mechanism.

It gives the model the ability to weigh relevance against every word in a sentence for every other word instead of looking strictly left to right the way older models did.

This is the mechanic allowing it to reference “it” back to the right noun three sentences prior, not lose it in the process.Training takes place in three stages, and it helps to know what contributes to what.

Pretraining comes first, and this is the part where the model learns to predict the next token by sifting through an enormous slice of the internet, books, and code.

Most of its raw ability comes from this bit, and it’s also the most expensive by a huge margin. Supervised fine-tuning is up next, and the model practices on curated examples of answers that are actually good, learning what a helpful answer looks like rather than just the statistically probable.

Then comes alignment, and methods like reinforcement learning from human feedback ensure outputs match what people prefer over what they do not.

Putting the three together and the result follows instructions. Leaving them out and you get a very confident autocomplete.

Large Language Model Examples and Applications

As of now the field includes GPT-family models from OpenAI, Claude from Anthropic, Gemini from Google, and open-weight alternatives like Llama and Mistral.

They vary in size, price, and capabilities, but they’re all built on the same core idea inside.Where do they actually show up? Customer support chatbots that can resolve tickets without touching a human.

Coding assistants that finish whole functions, not lines. Summarization tools that condense your contracts into three bullet points someone will actually read. Translations.

Content. Search that does what you meant rather than your keywords. A growing number actually house inside RAG-based assistants as well, which means these models provide answers based on a company’s own private documents rather than the training set it has.

A genuinely different skill from what the base model already has, and worth keeping in mind if you’re evaluating one for internal use at your own company.

Benefits of LLMsCoverage is probably the most obvious benefit.

One model can do writing, coding, and analysis, which saves teams having to maintain five separate systems for five separate functions.

Development also moves faster since there’s no training pipeline in between an idea and being able to test it same afternoon. The models continue to improve release over release, almost always without needing changes on your end.

Finally, since the same API can scale from one request to a million, growth rarely means you have to rebuild the system from scratch.

Limitations of LLMs

This is what most product pages forget to say.LLMs hallucinate, and can produce confident, fluent, completely wrong answers without it being obvious in what the answer even says.

They carry a training cutoff, and unless retrieval is attached they simply can’t know what happened yesterday. Running large models requires real money, and this bill escalates at scale.

Bias inherent to the training data shows up in the outputs, sometimes in ways no one can notice until a user points it out.

Context windows are always expanding, but it still has a cutoff on how much it can hold for any single answer.None of it precludes the usefulness of LLMs, but it certainly means a human reviewer is necessary when the stakes are real.

LLM vs Generative AI:

What’s the Difference Generative AI is the umbrella term for any model that creates content: text, images, audio, video, you name it.

An LLM is just a branch of that category, focused on language specifically.So every LLM qualifies as generative AI, but the converse does not.

A diffusion model is for generating images, and is not architecturally similar to a transformer-based language model.

LLM vs NLP:

What’s the Difference Natural Language Processing is the older, more broad field: teaching computers to deal with human language, however much that’s transformed over the lifetime of the field.

Rule-based parsers, statistical models, early neural networks like RNNs, all of it falls under the umbrella of NLP as well.

LLMs are just the dominant approach as of now inside NLP, not a replacement. Plenty of production systems utilize smaller, task-specific NLP models for things like classification or entity extraction, and that’s often the right choice,

since they’re cheaper and faster than running everything through a massive LLM, and require less data as well.

Best Practices for Getting Started with LLMs1.

Open a hosted chat model and play with it before doing anything else. Five minutes of hands-on poking is better than an hour of reading about it.

2. Read a few real model outputs and look for hallucination patterns. A pattern being obvious after you’ve seen one makes it much harder to picture when you’re only seeing a description.

3. Get comfortable with prompting basics before pitting different models against each other. A poor prompt makes all models look worse than what they actually should.

4. Match the model size to the task: small cheap models will get you through classification fine. Save the largest ones for reasoning that actually needs it.

5. Bring retrieval into the equation the moment accuracy on your own data becomes important.

6. Set expectations early with anyone adopting this process, especially on a team. LLMs assist, and aren’t a replacement for a human reviewer once something is high stakes.

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