How LLMs Actually Work: What Every Business Leader Needs to Know

Every week, another vendor pitches your team on "AI-powered" something. But how do you evaluate these claims when you don't understand the underlying technology? You don't need a PhD in machine learning — but you do need a working mental model of what large language models can and can't do.

This guide gives you that mental model. And if you want the full interactive experience, try our free How LLMs Work module — 45 minutes, 20 slides, no account needed.

What Is a Large Language Model?

A large language model (LLM) is a type of AI system that generates text by predicting what comes next in a sequence. Think of it as the world's most sophisticated autocomplete — trained on vast amounts of text from books, websites, code, and documents.

The key insight: LLMs don't "understand" language the way humans do. They've learned statistical patterns about which words tend to follow other words, and they're extraordinarily good at applying those patterns to generate coherent, useful text.

How Transformers Changed Everything

Before 2017, AI models processed text one word at a time, left to right. The breakthrough came with the transformer architecture, which lets models look at all words in a passage simultaneously.

This matters for business leaders because it explains why modern AI can:

• Summarize a 50-page report in seconds (it can "see" the whole document at once)
• Translate between languages while preserving nuance
• Write code by understanding the context of an entire codebase
• Answer questions about your company's documentation

Tokens: How AI "Reads"

LLMs don't actually read words — they process tokens, which are chunks of text (roughly 3/4 of a word on average). When you hear "GPT-4 has a 128K token context window," that means it can process roughly 96,000 words at once.

Why this matters for your business:

• Longer context = more capable. An AI that can read your entire policy manual at once is more useful than one limited to a few paragraphs.
• Token limits affect cost. Every token processed costs money when using AI APIs. Understanding this helps you budget AI projects accurately.

Training: Where the Knowledge Comes From

LLMs learn through a two-phase process:

Phase 1: Pre-training. The model reads billions of pages of text and learns general language patterns. This is expensive ($10M+ for frontier models) and done by companies like OpenAI, Anthropic, and Google.

Phase 2: Fine-tuning. The pre-trained model gets specialized training on specific tasks — following instructions, being helpful, avoiding harmful content. This is much cheaper and can be done on your own data.

For most businesses, you'll use pre-trained models (via APIs) rather than training your own. The key question isn't "should we train our own model?" but "how do we get the most value from existing models?"

What LLMs Can and Can't Do

They're excellent at:

• Drafting and editing text (emails, reports, documentation)
• Summarizing large documents
• Translating between languages
• Answering questions from provided context
• Generating code from descriptions
• Analyzing sentiment and categorizing text

They're unreliable at:

• Math and precise calculations (use a calculator instead)
• Providing real-time information (they have training cutoffs)
• Citing sources accurately (they "hallucinate" references)
• Making decisions that require domain expertise without guardrails
• Anything requiring physical world interaction

How to Evaluate AI Vendor Claims

Armed with this understanding, here's how to cut through vendor pitches:

1. Ask what model they use. If they won't tell you, that's a red flag. Most products use OpenAI, Anthropic, or Google models under the hood.
2. Ask about hallucination rates. Any vendor claiming "zero hallucinations" doesn't understand their own technology.
3. Ask about data privacy. Will your data be used to train the model? Where is it processed? This matters for compliance.
4. Ask for a proof of concept. The best way to evaluate AI is to test it on your actual use cases, not their demo data.

Your Next Step

Understanding how LLMs work is the foundation for every AI decision your organization will make. If you want to go deeper — with interactive slides, real-world examples, and a downloadable reference handout — take our free How LLMs Work module.

It's Module 1 of our AI Foundations Workshop, designed specifically for business leaders. No account needed, just your email. Takes about 45 minutes.

For teams that want the full program — including modules on identifying AI opportunities, build vs. buy decisions, AI governance, and hands-on demos — book a training consultation to discuss your needs.