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    What Is a Large Language Model (LLM)?

    A large language model (LLM) is a type of artificial intelligence trained on vast amounts of text to recognize patterns and predict what comes next, generating human-like responses in the process. Businesses rely on LLMs to give teams faster access to knowledge, which speeds up decision-making, and to automate routine content work.

    Expanded Definition

    LLMs are built using advanced machine learning techniques, especially deep learning, and are trained on billions of words of everyday text. That training lets them summarize, classify, translate, and generate new content, picking up on statistical patterns in language along the way.

    Unlike traditional AI systems with narrow rule sets, LLMs can adapt to many contexts, making them powerful for enterprise use. Larger models can pick up on subtler nuances and ambiguity in language, which supports more complex reasoning.

    The market for this game-changing technology is growing — Grand View Research estimates the global LLM market will expand from $5.62 billion in 2024 to $35.4 billion by 2030, a compound annual growth rate of nearly 37%.

    The cost of running these models is falling fast, too. Gartner forecasts that the cost of operating a large-scale LLM will drop by more than 90% by 2030, making the technology far more affordable to deploy at scale. That’s part of why LLM outputs increasingly show up inside everyday business intelligence tools, letting teams query data conversationally instead of writing code.

    Where does an LLM fit in the AI landscape?

    With all these AI terms circulating, it’s easy to lose track of how an LLM fits alongside other AI technologies.

    • Generative AI: An LLM is a type of generative AI focused on language. Generative AI as a whole also covers tools that create images, video, and audio.
    • Machine learning: Machine learning is the broader set of techniques that let computers learn from data instead of following fixed rules. LLMs are built using machine learning, at a much larger scale.
    • Natural language processing (NLP): NLP is the general goal of getting computers to understand and work with human language. An LLM is the main technology used to do that today, powering tasks like translation and sentiment analysis.
    • Retrieval augmented generation (RAG): RAG lets an LLM pull in an organization’s own data when answering a question, instead of relying only on what it learned during training.
    • AI agents: An AI agent uses an LLM to reason through a problem, but it also takes independent action, using tools and memory to complete multi-step tasks rather than just generating a response.
    • Chatbots: A chatbot is one type of application built on top of an LLM, using the model to interpret what a person types and generate a conversational reply. Not every chatbot runs on an LLM.

    How Large Language Models Are Applied in Business & Data

    LLMs have moved past one-off pilots and into daily workflows. They’re now handling the repetitive, language-heavy work that used to eat up analyst and knowledge-worker time, like summarizing long documents, answering routine questions, and turning unstructured text into something a team can act on instead of just read.

    The most common business use cases for LLMs include the ability to:

    • Automate customer service: Power chatbots and virtual assistants that resolve routine requests without a live agent.
    • Draft content at scale: Generate reports, marketing copy, blog posts, and technical documentation to cut down on manual writing time.
    • Support knowledge management: Make unstructured text searchable and actionable, so teams can find answers in seconds instead of digging through files.
    • Enhance analytics workflows: Translate natural language questions into queries and models, so people can ask for insights in plain English.
    • Improve data governance and compliance: Scan text for risk, sensitive data, or regulatory issues before they become a problem.
    • Personalize customer content: Produce tailored product descriptions and localized copy for different markets.
    • Widen data accessibility: Let non-technical teams query data in plain language, closing the gap between raw data and the people who need to act on it.

    Alteryx enables enterprises to operationalize AI capabilities, including LLMs, by connecting them to governed data pipelines, so outputs stay accurate and auditable while scaling across the business.

    The business case for pairing LLMs with governed data is showing up in the numbers. McKinsey’s 2025 State of AI survey found that 88% of organizations now report regular AI use in at least one business function, up from 78% the year before, and Gartner notes that enterprise spending on AI models alone is on pace to more than double in 2026. For most companies, the bottleneck isn’t access to an LLM anymore. It’s making sure that the data feeding the LLM is clean and current and something teams can actually trust.

    Types of LLM deployment models

    An LLM, like GPT-4 or Llama, is the AI model itself. How a business accesses or runs that model is a separate decision, called its deployment approach, and it usually comes down to one of three options.

    • Public LLMs: General-purpose models, like those behind popular consumer chatbots, accessed through an application programming interface (API). These are fast to adopt but typically don’t retain an organization’s proprietary context between sessions.
    • Private or self-hosted LLMs: The same kind of model, deployed within an organization’s own infrastructure or a private cloud environment instead of a public API, giving tighter control over where data lives and how it’s secured.
    • Fine-tuned LLMs: A public or self-hosted model that’s been further trained on a company’s own data set to improve accuracy on domain-specific tasks, like legal contract review or insurance claims processing. This is often how companies get accurate, on-brand output without training a model from scratch.

    The Alteryx platform connects any of these approaches to governed data pipelines, so whichever model a team chooses always works with clean, permissioned data that’s kept current.

    Common challenges with LLMs

    Every deployment option above still runs into a few of the same challenges, and it’s worth planning for these upfront rather than after rollout.

    • Bias: An LLM can reflect patterns and biases present in its training data, which can surface in its outputs if left unchecked.
    • Data privacy and security: Sending sensitive information to a public LLM API can raise compliance and confidentiality concerns, which is one reason many businesses choose private or self-hosted deployment instead.
    • Outdated knowledge: An LLM’s training has a cutoff date, so it won’t know about anything that happened after that point unless it’s connected to current data, often through RAG.

    How Large Language Models Work

    LLMs process text step by step: breaking it into smaller pieces, learning the relationships between those pieces, and predicting what comes next. Knowing this flow also explains why an LLM can sound completely confident even when it’s wrong.

    Here’s how a large language model generates text, step by step:

    1. Text is broken into tokens: Words or pieces of words are split into small units called tokens.
    2. Tokens are converted into numbers: Each token becomes a numerical representation so the model can process it mathematically.
    3. The model learns relationships: Using a transformer architecture, the model identifies patterns and connections between tokens, learning how words relate to each other in context.
    4. Prediction happens step by step: When generating a response, the model predicts the most likely next token, one at a time, to build sentences and paragraphs.
    5. Scale improves performance: Larger models, with more parameters and broader training data, tend to deliver more accurate results, especially once they’re fine-tuned for a specific industry or task.

    Use Cases

    The right LLM use case looks different depending on which team is using it. A marketer wants faster content, while a legal team wants faster contract review, but both are drawing on the same underlying technology.

    Common LLM use cases by business function include:

    • Marketing: Draft campaign copy and localize messaging for different markets, generating variations to test in a fraction of the usual time.
    • Customer support: Power chatbots and agent-assist tools that resolve routine tickets and summarize long conversation threads.
    • Sales: Summarize call transcripts and draft follow-up emails, so reps spend less time on paperwork and more time selling.
    • Legal and compliance: Review contracts and flag risky language, cutting down review cycles on regulatory documents.
    • Human resources: Draft job descriptions and summarize resumes, then let an internal assistant field employee policy questions.
    • IT and data teams: Generate documentation and explain workflows in plain language, translating natural language requests into queries along the way.

    Industry Examples

    Document-heavy, highly regulated industries tend to see the fastest payoff from LLMs, since so much of the daily work involves reading, summarizing, or responding to large volumes of text.

    Here’s how different industry sectors use LLMs:

    • Healthcare: Assist clinicians with medical literature searches and summarizing patient histories.
    • Insurance: Automate claims processing through document analysis.
    • Public sector: Help agencies respond to citizen inquiries through natural language self-service portals.
    • Finance: Simplify fraud detection, credit decisions, risk management, and compliance reviews.

    Frequently Asked Questions

    Are LLMs always accurate, or do they hallucinate?

    LLMs can generate plausible but incorrect outputs, often called hallucinations. Enterprises mitigate this by combining LLMs with verified data sources and human review.

    Do LLMs replace human analysts?

    They augment human work by accelerating routine tasks and freeing up time for deeper analysis and strategic thinking. People are still necessary to evaluate LLM output for correctness and bias, and to keep governance in place.

    How much does it cost to use an LLM?

    It depends on the deployment approach. Public LLM APIs typically charge by usage, so costs start low but climb as query volume grows. Self-hosting or fine-tuning a model shifts the spending toward infrastructure and setup instead of per-query fees, which can pay off at scale. For most companies, though, the bigger cost driver isn’t the model itself. It’s the time spent cleaning and preparing the data that feeds it, which is exactly the problem that a governed data pipeline is built to solve.

    What are some examples of large language models?

    GPT-4, Gemini, Claude, and Llama are among the best-known LLMs, though most are frontier models built by a handful of large AI labs. Businesses don’t need to build one from scratch. They can access these models through an API, or run open-source versions within their own infrastructure.

    Further Resources

    Sources and References

    Synonyms

    • Foundation model
    • Generative language model
    • Transformer-based model

    Related Terms

    Last Reviewed: August 2026

    Alteryx Editorial Standards and Review
    This glossary entry was created and reviewed by the Alteryx content team for clarity, accuracy, and alignment with our expertise in data analytics automation.