What Are LLMs?
- What large language models are and how they work at a high level
- Why LLMs are so good at understanding and generating text
- The strengths and limitations business leaders need to know
- How LLMs power real business automation today
If you've used ChatGPT, asked Siri a question, or seen AI draft an email, you've interacted with a large language model. LLMs are the engine behind most of the AI tools making waves in business today. But what are they, really?
This guide explains LLMs in plain language -- no computer science degree needed. You'll walk away understanding what they do, why they're useful, and where they fall short.
The Core Idea
A large language model is software that has been trained on enormous amounts of text -- books, articles, websites, code -- until it develops an understanding of how language works. Not just grammar. It learns meaning, context, and relationships between ideas.
Think of it like this: if you've read thousands of business proposals, you start to recognise patterns. You know what a good executive summary looks like. You can predict what section comes next. You can spot when something doesn't make sense.
LLMs do the same thing, but at a scale no human could match. They've processed billions of documents, and that training lets them understand, generate, and reason about text with remarkable accuracy.
An LLM is not a search engine or a database. It's a pattern-recognition system that understands language well enough to read, write, summarise, and reason about text -- much like a very fast, very well-read colleague.
What Makes LLMs "Large"?
The "large" in LLM refers to two things:
Training data -- Modern LLMs are trained on datasets that span a significant portion of publicly available text on the internet, plus books, academic papers, and code repositories.
Model size -- The models themselves contain billions of parameters (think of these as the "neurons" that store learned patterns). More parameters generally means better understanding of nuance and context.
| Model Characteristic | What It Means for You |
|---|---|
| Training data size | More data = broader knowledge. The model has likely "seen" your industry's terminology and patterns. |
| Parameter count | More parameters = better nuance. It can distinguish between "bank" (finance) and "bank" (river) from context. |
| Context window | How much text it can consider at once. Larger windows mean it can process longer documents. |
| Fine-tuning | Models can be specialised for your domain, improving accuracy on your specific tasks. |
What LLMs Can Actually Do
Here's where it gets practical. LLMs are surprisingly versatile, and these capabilities map directly to business operations:
Read and Understand
LLMs can read an email, a contract, a support ticket, or a Slack message and understand what it's about. Not just keyword matching -- genuine comprehension of intent, sentiment, and context.
Write and Respond
They can draft replies, write summaries, create reports, and generate content that reads naturally. The output quality depends on how well you describe what you want.
Classify and Route
Given a piece of text, an LLM can categorise it: "This is a billing complaint." "This is a feature request." "This is urgent." This makes them excellent for triage workflows.
Extract and Structure
LLMs can pull structured data from unstructured text. Read a rambling email and extract the sender's name, company, request type, and deadline into clean fields.
Reason and Decide
They can evaluate information against criteria and make recommendations. "Based on this deal's history and the email tone, the risk level is medium and the recommended action is..."
Outrun's AI Workflow Builder puts these LLM capabilities into visual workflows. You can build a flow that reads incoming emails, classifies them, extracts key data, and routes them to the right team -- all without writing a single line of code. See the AI Workflow Builder feature page to explore templates.
What LLMs Cannot Do
Being honest about limitations is just as important as understanding capabilities. Here's where LLMs fall short:
They don't "know" facts. LLMs generate responses based on patterns in their training data. They can produce confident-sounding answers that are factually wrong. This is called "hallucination." For business-critical decisions, LLM outputs should be verified or grounded in your actual data.
They don't learn from your conversations. Unless specifically set up to do so, an LLM doesn't remember what you told it yesterday. Each interaction starts fresh.
They struggle with maths and precise logic. LLMs are language models, not calculators. They can reason about business problems, but you wouldn't want one doing your financial reconciliation.
They don't access real-time information. A base LLM only knows what was in its training data. To work with your live data, it needs to be connected to your systems -- this is where platforms like Outrun come in.
Understanding LLM limitations protects you from bad vendor pitches. If someone promises you an AI that "never makes mistakes" or "learns everything automatically," they're either overselling or don't understand the technology. The best AI implementations are designed around these limitations, not in denial of them.
LLMs in the Real World: Business Use Cases
Here's how LLMs are being used in business operations right now:
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Sales email triage -- An LLM reads every incoming email, identifies which ones are from prospects, extracts their intent, and routes them to the right sales rep with a suggested response.
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CRM enrichment -- Instead of manual data entry, an LLM reads email threads and call transcripts to keep your CRM updated with the latest contact details, deal stages, and next steps.
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Support ticket classification -- LLMs categorise support tickets by urgency, topic, and customer tier, then route them to the right team with relevant context attached.
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Document analysis -- Contracts, proposals, and reports get summarised into actionable briefs so your team can make decisions faster.
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Internal knowledge queries -- Employees ask questions in natural language and get answers drawn from your company's documentation, policies, and past decisions.
How LLMs Fit Into Your Tech Stack
You don't need to run your own LLM. Modern business platforms integrate LLMs behind the scenes. You interact with the capabilities -- email triage, data extraction, workflow automation -- while the platform handles the AI infrastructure.
The key questions when evaluating any AI-powered tool:
- Which LLM does it use? Different models have different strengths. Some are better at reasoning, others at following instructions precisely.
- How does it access my data? The LLM needs context about your business to be useful. Look for secure integrations with your existing tools.
- What guardrails are in place? How does the platform handle hallucinations? Is there human review for critical actions? Are there audit trails?
What's Next
Now that you understand what LLMs are and what they can do, the next guide covers the broader category they belong to.
In What is Generative AI?, you'll learn how LLMs fit into the wider landscape of AI that creates content -- and why this distinction matters when you're evaluating tools for your team.
