---
title: What Is an LLM? (And Why Should You Care?)
description: Plain-English guide to large language models for marketing and sales pros. What they are, how they work, and why it matters for your job.
date: January 20, 2026
author: Robert Soares
category: ai-fundamentals
---

An LLM is autocomplete on steroids. That's the short version.

When you type a text message and your phone suggests the next word? Same idea. Except an LLM does it with billions of parameters, trained on a huge chunk of the internet, predicting what word comes next over and over until you have entire paragraphs.

LLM stands for Large Language Model. "Large" refers to the size. GPT-4, for example, reportedly has around [1.8 trillion parameters](https://en.wikipedia.org/wiki/GPT-4) trained across 13 trillion tokens of text. "Language Model" describes what it does: predict and generate human language.

Simple concept. Surprisingly powerful results.

## How Does an LLM Actually Work?

Here's the thing most explanations get wrong: LLMs aren't thinking. They're predicting.

According to [IBM's explanation of large language models](https://www.ibm.com/think/topics/large-language-models), an LLM works by receiving an input, encoding it, and then decoding it to produce an output prediction. At its core, it's asking: "Given all the words so far, what word most likely comes next?"

When you give an LLM a prompt like "Write me a cold email to a VP of Marketing," it doesn't understand what marketing is. It doesn't know what a VP does. What it does know is that in its training data, when text starts like that, certain patterns of words tend to follow.

It generates the response one token at a time. (Tokens are usually pieces of words, roughly 3-4 characters each.) For every token, it calculates probabilities for thousands of possible next tokens, picks one, then repeats. Billions of times.

This is why LLMs can write but struggle with math. They're not calculating. They're pattern-matching based on what they've seen before.

Think of it like this: if you've read thousands of cookbooks, you can write something that looks like a recipe. You know that recipes start with ingredients, then instructions, and use words like "combine," "stir," and "bake." But you've never actually cooked anything. You don't know if the recipe would taste good. You're just producing text that matches the pattern.

That's what LLMs do, but at an enormous scale.

## The Technology Behind It: Transformers

The neural network architecture that makes modern LLMs possible is called a transformer. Google researchers introduced it in a [2017 paper titled "Attention Is All You Need"](https://arxiv.org/abs/1706.03762).

Before transformers, language models processed words sequentially. One at a time, in order. That made them slow and bad at connecting ideas that were far apart in a sentence.

Transformers use something called self-attention. This lets the model process all words in a sequence simultaneously and figure out which words relate to which other words, regardless of how far apart they are. The sentence "The dog that the woman who lives in the blue house on the corner adopted barked" is confusing for older models. A transformer can track that "barked" connects to "dog" even with all those words in between.

You don't need to understand the math. What matters is that transformers made LLMs practical. They train faster and handle context better than anything that came before.

## What Can LLMs Actually Do?

The list keeps growing, but here's where LLMs genuinely help in 2026:

**Writing assistance.** First drafts of emails, blog posts, proposals, social media content. This is where most people start, and where the time savings are obvious.

**Summarization.** Drop in a 50-page report, get the key points in two paragraphs. Models like [Llama 4 Scout now support up to 10 million tokens of context](https://ai.meta.com/blog/llama-4-multimodal-intelligence/), meaning they can process enormous documents in one go.

**Research and analysis.** Ask questions about your data, get summaries of competitive intel, synthesize information from multiple sources.

**Brainstorming.** Generate ten angles for a campaign, twenty subject lines for an email, variations on a positioning statement. Not all of them will be good. But having options beats staring at a blank page.

**Translation and localization.** Not just word-for-word translation, but adapting content for different markets and tones.

**Code and technical work.** Even if you're not a developer, LLMs can help with formulas, scripts, and automation tasks. Models like [GPT-5.2-Codex](https://openai.com/index/introducing-gpt-5-2-codex/) are specifically optimized for this.

**Reasoning and problem-solving.** Newer models have dedicated "thinking" modes that work through problems step by step. Ask a reasoning model to analyze why a campaign underperformed, and it will lay out its logic rather than just give you an answer. This is one of the bigger shifts in 2025-2026. Models aren't just generating text anymore. They're generating explanations.

## What LLMs Can't Do (Yet)

Understanding what LLMs can't do matters as much as knowing what they can. This saves you from the frustrating "why isn't this working?" moments.

**They can't verify truth.** LLMs don't fact-check. They generate text that looks plausible based on patterns. If those patterns include false information, the output includes false information. [OpenAI's research on hallucinations](https://openai.com/index/why-language-models-hallucinate/) explains that hallucinations happen partly because training methods reward guessing over acknowledging uncertainty.

**They don't have access to current information.** Most models have a knowledge cutoff date. GPT-5.2 models have a cutoff of August 2025, [according to OpenAI](https://openai.com/index/introducing-gpt-5-2/). Anything after that date is unknown to the model unless you provide it as context or the model can search the web.

**They're not consistent.** Ask the same question twice, get two different answers. This is by design. The models introduce some randomness to avoid being predictable.

**They can't replace expertise.** An LLM can write something that sounds like expert marketing copy. That's not the same as actually being an expert. The output needs human review from someone who knows the subject.

**They don't know your specific situation.** Unless you tell them. An LLM doesn't know your company's brand voice, your product's specific features, or your customer's particular pain points. It only knows what's in its training data plus whatever context you provide. The more specific context you give, the more useful the output becomes. But if you ask for "marketing copy," you'll get generic marketing copy.

## The Hallucination Problem

This one deserves its own section because it trips up even experienced users.

LLMs sometimes generate completely false information with total confidence. [IBM describes AI hallucinations](https://www.ibm.com/think/topics/ai-hallucinations) as false or misleading information presented as fact. A lawyer submitted a legal brief written by ChatGPT to a Manhattan federal judge in 2023. The AI had made up court cases that didn't exist. A judge sanctioned the lawyers involved.

Why does this happen? Because the model doesn't know what's true. It knows what sounds like it should come next based on patterns. If there's a gap in the training data, or if the prompt creates unusual conditions, the model fills in the blank with something plausible-sounding.

The newer models are better at this. [GPT-5.2 reportedly reduced hallucination rates to 6.2%](https://openai.com/index/introducing-gpt-5-2/), about a 40% improvement from earlier versions. But "better" isn't "gone." You still need to verify anything an LLM tells you, especially facts, figures, and references.

The practical response: treat LLM output like a first draft from a smart but careless intern. They probably got the structure right. The specifics need checking.

## The Current LLM Landscape (January 2026)

The major players right now:

**OpenAI's GPT-5.2** is the latest from OpenAI, available in three tiers: Instant (fast everyday tasks), Thinking (harder work with more polish), and Pro (the most capable, worth the wait for complex questions). The [GPT-5.2 announcement](https://openai.com/index/introducing-gpt-5-2/) highlights significant improvements in reasoning and reduced errors.

**Anthropic's Claude** sits at version 4.5 for the top-tier Opus model. Claude has a reputation for handling nuance well and excels at long-form analysis. [Claude Opus 4.5](https://www.anthropic.com/claude/opus) and Sonnet 4 both support context windows up to 1 million tokens.

**Google's Gemini 3** launched in November 2025 and [outperformed other major models in most benchmarks tested](https://en.wikipedia.org/wiki/Gemini_(language_model)). Google is positioning Gemini for complex multimodal tasks, handling text, images, and video together.

**Meta's Llama 4** continues the open-source approach. [Llama 4 Scout](https://ai.meta.com/blog/llama-4-multimodal-intelligence/) offers a 10-million-token context window. The models are free to use, which makes them popular for companies building their own AI applications.

**DeepSeek and Mistral** offer cost-effective alternatives. These matter if you're using AI at scale, where token costs add up fast.

Which one is "best"? Depends on what you're doing. For writing that needs polish and nuance, Claude tends to shine. For speed and bulk work, GPT is often faster. For huge documents or multimodal work, Gemini handles it well. For free or self-hosted options, Llama is the go-to. The differences matter less than they used to. All the major models are capable. Pick one, learn it, switch if you hit limitations.

## Why Should Marketing and Sales People Care?

Most sales and marketing teams are already using AI tools. [McKinsey's 2025 State of AI report](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) found that 88% of organizations now report regular AI use in at least one business function, up from 78% the year before.

But here's the part that gets less attention: adoption isn't the same as results.

That same McKinsey research found that nearly two-thirds of organizations haven't begun scaling AI across the enterprise. Just 39% report meaningful impact on their bottom line. [Bain's 2025 technology report](https://www.bain.com/insights/from-pilots-to-payoff-generative-ai-in-software-development-technology-report-2025/) noted that even when teams see 10-15% productivity gains from AI assistants, those gains often don't translate into business results because the time saved isn't redirected to higher-value work.

What separates the teams that get value from the ones that don't?

Understanding what the technology actually is helps. When you know that an LLM is predicting words based on patterns rather than "understanding" your business, you approach it differently. You verify outputs. You provide better context in your prompts. You use it for the tasks where prediction and pattern-matching genuinely help.

The companies seeing real value tend to share a few things in common. They train people on the tools. They measure specific outcomes, not just "we're using AI." They redesign workflows rather than bolting AI onto existing processes. [Bain's research](https://www.bain.com/insights/from-pilots-to-payoff-generative-ai-in-software-development-technology-report-2025/) found that companies successfully scaling AI in sales and marketing see 30-50% reductions in time spent on content creation. But those gains require more than just signing up for a chatbot.

## Practical First Steps

If you're new to working with LLMs directly, here's where to start:

**Pick one task.** Don't try to "use AI for everything." Choose a specific, repeating task. First drafts of cold emails. Weekly report summaries. Social post variations. One thing.

**Give it context.** LLMs work better when you explain who you are, who you're writing for, and what you're trying to accomplish. "Write a sales email" gets generic output. "Write a sales email from a B2B SaaS company selling project management software to a VP of Operations at a mid-size manufacturing company who has expressed frustration with missed deadlines" gets something useful.

**Check the output.** Every time. Until you have a feel for where the model succeeds and where it struggles, verify everything. Especially facts, names, and anything you'd be embarrassed to get wrong.

**Iterate.** If the first output isn't right, don't start over. Say what's wrong and ask for changes. "Make it shorter." "More casual tone." "Focus on the deadline problem, not the cost savings." The model improves with feedback in the same conversation.

**Save what works.** When you get a good result, save the prompt. Note what context you provided. Over time, you build a personal library of prompts that work for your specific use cases. This is more valuable than any "ultimate prompt guide" you'll find online.

## The Technology Is Moving Fast

The LLM space changes quickly. What's true in January 2026 might look different by summer.

The models are getting better at reasoning. They're handling longer documents. Hallucination rates are dropping. Costs keep falling.

But the fundamentals haven't changed. LLMs predict text based on patterns. They don't understand, they don't verify, they don't have opinions. They're tools that amplify what you bring to them.

Understanding that is the starting point for actually getting value from them.