What are large language models: A practical, concise guide to AI

Discover what are large language models and how they work, their real-world applications, and implications for business and society.

Large language models (LLMs) are a type of AI that has been trained on truly massive amounts of text data to understand, generate, and interact with human language. Think of them as expert apprentices who have read a library the size of the internet. This allows them to write, summarize, code, and handle complex language tasks with an impressive, almost human-like fluency.

Decoding Large Language Models

A person typing on a laptop next to a stack of books, with a sign reading 'WHAT ARE LLMS' on a wooden desk.

At its core, you can think of a large language model as an autocomplete function on steroids. When you type a search into Google, it suggests the rest of your query based on what others have searched for. An LLM works on a similar principle but at an astronomical scale—it predicts the next word in a sentence based on the context of all the words that came before it.

This skill is developed through a training method called deep learning. The model is fed trillions of words from books, articles, websites, and more. By poring over this data, it starts to pick up on grammar, facts, patterns of reasoning, and even subtle nuances in writing style. It isn't just memorizing text; it's learning the intricate relationships between words and concepts.

The "large" part of the name comes from the huge number of parameters the model uses. Parameters are the internal variables that the AI adjusts during its training, acting like tiny knobs that fine-tune its understanding of language. Early models might have had millions of parameters, but today's leading systems like GPT-4 work with billions, giving them a much deeper and more sophisticated grasp of context and meaning.

Large Language Models at a Glance

To make this even clearer, let's break down the core ideas behind LLMs and why they're becoming so important for businesses.

Core Concept	What It Does	Business Impact
Deep Learning	Trains on massive text datasets to spot patterns, context, and word relationships.	Helps the AI understand complex customer questions, market trends, and internal reports.
Parameters	Acts as the model's internal 'knowledge base' that gets fine-tuned during training.	Generally, more parameters mean a more capable and nuanced model for specialized tasks.
Text Generation	Predicts the most likely next word in a sequence to create coherent, natural-sounding text.	Automates content creation, drafts emails, generates reports, and fuels chatbot conversations.

This table gives a quick snapshot, but the real impact becomes clear when you see these models in action.

The Soaring Business Potential

The business world is certainly paying attention. The market for large language models is on an incredible growth trajectory, projected to jump from around USD 6.5 billion in 2024 to a massive USD 140.8 billion by 2033. That growth is driven by a compound annual growth rate (CAGR) of 40.7%, which signals a major shift in how companies get work done. You can explore more data on the LLM market growth and what it means for businesses.

This explosive trend makes one thing clear: for business leaders, figuring out what large language models are is no longer an academic exercise. It's a strategic must.

A large language model isn't just a text generator; it's a completely new way to access and interact with information. For businesses, this opens the door to unlocking insights from messy data, automating entire workflows, and building customer experiences that were never possible before.

From smart chatbots that solve customer problems in real-time to tools that can summarize dense legal documents in seconds, LLMs are already being used to tackle real-world business challenges. This guide will give you the foundational knowledge you need to see their strategic value and get your organization ready to put them to work.

How LLMs Actually Learn and Think

It’s tempting to think of large language models as "thinking" machines, but that’s not quite right. They don’t possess consciousness or understanding in the way humans do. Instead, their intelligence is the result of incredibly sophisticated pattern recognition, honed through a training process that’s a lot like someone mastering a new skill—not by memorizing facts, but by practicing until the core principles become second nature.

This process is what turns a blank slate of an algorithm into a powerful tool for generating and understanding language.

The engine making this all possible is the transformer architecture. Introduced back in 2017, this groundbreaking design gave models a new superpower called self-attention.

Here’s a simple way to think about self-attention. When you read the sentence, "The delivery truck blocked the street, so it was late," you instantly know "it" refers to the truck, not the street. Self-attention gives an LLM that same ability. It weighs the importance of every word against all the other words in a sentence, no matter how far apart they are. This knack for grasping context is what makes modern LLMs so incredibly capable.

The Two-Stage Training Process

An LLM's education happens in two key phases. Each stage has a different goal, taking the model from a generalist with broad knowledge to a specialist ready for specific business tasks.

Pre-training: The Digital Library Phase
This is where the model builds its foundational understanding of language. It’s fed a mind-bogglingly large dataset—think a huge chunk of the public internet, from books and articles to websites. The task is simple: predict the next word in a sentence. After doing this billions and billions of times, it starts to internalize grammar, facts, reasoning styles, and the subtle ways concepts connect.
Fine-tuning: The Specialization Phase
After pre-training, the model is a jack-of-all-trades but a master of none. Fine-tuning is what shapes it for specific jobs. This involves training it on a much smaller, high-quality dataset designed for a particular purpose, like analyzing legal contracts or handling customer support chats. Human reviewers often step in here to guide the process, reinforcing good answers and flagging bad ones to make sure the model’s behavior aligns with what you actually want it to do.

The goal of training isn't just to absorb information but to build a model that can generalize from what it has learned. A well-trained LLM can apply its knowledge to generate entirely new, relevant text for tasks it wasn't explicitly trained on.

Parameters: The Building Blocks of Knowledge

So, what does the "large" in large language model actually mean? It refers to its parameters—the internal variables the model adjusts during training. These parameters, often numbering in the billions, are where the learned patterns and relationships are stored. They form the model's knowledge base.

Generally, more parameters allow for a more nuanced and capable model. But bigger isn't always better.

One of the most surprising trends in AI is the growing efficiency of these models. For instance, models with under 100 billion parameters are projected to capture a whopping 69.20% of the market revenue in 2025. You can dig into the numbers yourself by reviewing the latest market intelligence on LLM adoption. This trend is making advanced AI accessible even to mid-sized companies that don't have massive GPU farms.

This has led to a boom in specialized models that perform exceptionally well on targeted tasks without the massive overhead. For businesses, this means you can get powerful results without needing the biggest, most resource-hungry model out there. Sometimes, you just need a model that can securely access your company's private data; you can find out more about that in our guide on what is retrieval-augmented generation. This strategic approach is the key to unlocking what LLMs can do without breaking the bank.

Choosing the Right Type of Large Language Model

Not all LLMs are built for the same job, and picking the right one is a huge strategic decision. Think of it like choosing a vehicle: you wouldn't use a Formula 1 race car to haul lumber, and you wouldn't take a semi-truck on a scenic mountain pass. The best choice always comes down to the task at hand.

The LLM world is much the same. It's split into different categories, and each one has its own unique strengths. The first big divide is between the generalists and the specialists.

General-Purpose vs Specialized Models

General-purpose models, like OpenAI's GPT-4 or Google's Gemini, are the versatile Swiss Army knives of the AI universe. They’ve been trained on a massive, diverse slice of the internet, so they can tackle an incredible range of tasks right out of the box. You can ask them to write marketing copy, draft an email, translate a document, or even spit out some code.

Their broad knowledge makes them incredibly powerful for all sorts of business functions. They’re a fantastic starting point for any company just beginning to explore what LLMs can do, especially if you don’t have a super-specific application in mind yet.

On the other hand, specialized models are experts fine-tuned for a specific domain. These models usually start their life as generalists but then go through extra training on a curated, high-quality dataset for a particular field—think finance, healthcare, or law.

A legal LLM, for example, would be trained extensively on case law and legal documents, making it way more accurate at contract analysis than a general-purpose model could ever be. This kind of specialization delivers far greater precision and reliability for niche tasks.

This flowchart shows the journey from a broad, general model to a focused, expert tool.

Diagram illustrating the steps of Large Language Model training: pre-training, fine-tuning, and specialization for niche applications.

This process is all about refining a jack-of-all-trades into a master of one, moving from broad knowledge to deep, domain-specific expertise.

Open-Source vs Closed-Source Models

Beyond specialization, another critical fork in the road is deciding between a proprietary, closed-source model and an open-source alternative. This choice has massive implications for your costs, control, and security.

Closed-source models, also called proprietary models, are developed and owned by a single company (like OpenAI or Google). You usually access them through an API, paying for what you use. They're powerful, easy to get started with, and the vendor handles all the heavy lifting on the backend. But the trade-off is that you have limited control and have to trust the provider with your data.

Open-source models, like Meta's Llama 3 or models from Mistral, offer a totally different path. Their underlying code is public, so anyone can download, modify, and run them on their own servers. This gives you maximum control, customization, and data privacy, but it also demands serious technical skill, computational power, and in-house talent to manage.

This table breaks down the strategic differences between the two, helping you weigh the pros and cons for your specific needs.

Open-Source vs Closed-Source LLMs: A Strategic Comparison

Attribute	Open-Source LLMs (e.g., Llama 3)	Closed-Source LLMs (e.g., GPT-4)
Data Control	Complete control. Data stays on your servers.	Data is processed by a third-party vendor.
Customization	Hugely customizable. Fine-tune it for anything.	Limited to what the provider’s API allows.
Cost Model	No license fees, but expect high compute costs.	Pay-per-use. Can get expensive with heavy use.
Maintenance	All on you. Requires an expert in-house team.	Zero maintenance. Managed entirely by the vendor.
Accessibility	Needs technical setup, deployment, and infrastructure.	Simple to access through a straightforward API.

Ultimately, there’s no single "best" model. The right choice is the one that aligns with your company's security policies, budget, and long-term goals. Getting this decision right is the key to successfully bringing LLMs into your business.

Real-World LLM Applications Driving Business Value

Professionals analyze business data on laptops and computers in a warehouse and office setting.

The theory behind large language models is fascinating, but their true impact hits home when they move from the lab into the real world. Companies across every industry are putting LLMs to work to solve concrete problems, automate painful workflows, and generate real financial returns.

These aren't just futuristic concepts anymore. They are practical tools delivering measurable value today, from financial services to healthcare. The core language abilities of these models are being molded to fit specific business needs, giving a clear competitive edge to organizations that get it right.

Automating High-Stakes Financial Reporting

The world of finance runs on precision, compliance, and mountains of paperwork. Historically, this has meant thousands of hours of manual work from highly skilled professionals, especially for tasks like creating compliance reports or auditing financial statements.

Large language models are completely changing that game. A financial firm can now use a fine-tuned LLM to:

Scan regulatory documents to instantly spot changes and keep internal policies up-to-date.
Whip up initial drafts of quarterly earnings reports by pulling together performance data from different systems.
Automate compliance checks by flagging risky keywords or patterns in internal communications.

This doesn't just save a massive amount of time; it also cuts down on human error in mission-critical processes. It frees up analysts to think about strategy instead of getting buried in routine paperwork.

Creating Hyper-Personalized Customer Journeys

In the cutthroat world of e-commerce, personalization is everything. Generic marketing blasts just don't work anymore. LLMs are letting businesses create customer experiences that feel like they were built for one person, leading to big jumps in engagement and sales.

For example, an online brand can use an LLM to supercharge its customer service and marketing. Using AI customer support software allows for instant, personalized help that was previously impossible to deliver at scale.

Beyond support, LLMs can also:

Write dynamic product descriptions that highlight the features a specific shopper actually cares about based on their browsing.
Craft personalized email campaigns that go way beyond just using a customer's name, adapting the tone and offers to their purchase history.
Power smart chatbots that understand complex questions and give genuinely helpful answers, 24/7.

This move from mass marketing to mass personalization builds stronger customer loyalty and has a direct impact on the bottom line. It's a perfect example of the many real-world natural language processing applications businesses are now embracing.

At its core, an LLM’s business value comes from its ability to understand and work with unstructured language at a scale and speed humans simply can't match. This unlocks insights and automates tasks that were once too complex or time-consuming to bother with.

Accelerating Insights in Healthcare and Life Sciences

Healthcare is another industry drowning in text. Patient records, clinical trial data, and medical research papers are filled with priceless information, but it's often trapped in unstructured notes and documents.

Large language models are helping break down those walls. A hospital can use an LLM to summarize a patient’s entire medical history in seconds, pulling key details from doctors' notes, lab results, and specialist reports. This gives clinicians a complete picture almost instantly, helping them make faster diagnoses and provide better care.

This technology is spreading fast. In North America, which holds a dominant 32.7% market share, tech giants and startups are weaving LLMs into everything from customer service bots to automated content creation. These integrations are driving huge operational efficiencies and saving companies millions in labor costs, underscoring the real, tangible impact LLMs are having on business right now.

To get the most out of large language models, you have to look at them with a healthy dose of realism. While they're incredibly powerful, they're far from perfect. Understanding both their game-changing advantages and their built-in weaknesses is the key to setting realistic expectations and building a smart adoption strategy.

The upsides are pretty clear and often immediate. The biggest one is a massive boost in efficiency. LLMs can take over routine tasks like drafting emails, summarizing lengthy reports, or writing starter code. This frees up your skilled people to focus on the strategic work that actually moves the needle, leading to real cost savings and faster project timelines.

They also act as fantastic engines for creativity and discovery. You can use an LLM to brainstorm marketing slogans, generate new product ideas, or help researchers tear through decades of academic papers in a fraction of the time. They’re also brilliant at unlocking the value buried in huge piles of unstructured data—like customer reviews or internal docs—by pulling out key themes and sentiments that would be impossible for a human to spot.

The Major Benefits of LLMs

Workflow Automation: LLMs can handle repetitive language-based jobs, from standard customer service replies to generating meeting minutes. This cuts down on manual work and keeps things consistent.
Augmented Creativity: By spitting out diverse ideas and content, they become a valuable partner for creative folks in marketing, design, and product development.
Insight Extraction: They can chew through enormous amounts of text to spot trends, gauge customer sentiment, and flag business risks you might have otherwise missed.

But for all the good they can do, you have to confront their limitations head-on. One of the biggest risks is what the industry calls model "hallucinations." This is when an LLM confidently states something that is factually wrong or completely made up. Because the output sounds so plausible, it can easily mislead people if it’s not carefully double-checked.

A large language model doesn't "know" facts the way a person does. It just predicts the next most likely word based on patterns in its training data. Sometimes, that process leads it to build sentences that sound right but are totally false.

Understanding the Key Limitations

Another huge challenge is inherent bias. An LLM learns from the data it's trained on, which is basically a giant snapshot of the internet. That means it can accidentally absorb and repeat all the societal biases related to race, gender, and culture found in that data. If you don't keep an eye on this, it can lead to unfair or offensive outputs, creating some serious reputational and legal headaches for your business.

Finally, the computational cost of training and running these models is no joke. Operating large-scale LLMs requires a staggering amount of processing power, which translates into high energy use and hefty cloud computing bills. For smaller companies, these operational costs can be a major hurdle, making careful planning an absolute must.

A clear-eyed view of both the benefits and the risks is the only way forward. By anticipating these challenges, you can build in safeguards—like human-in-the-loop verification and regular bias audits—to tap into the power of LLMs while keeping the potential harm to a minimum.

Your Enterprise Roadmap for LLM Adoption

Overhead view of four people collaborating on a strategic 'Adoption Roadmap' document on a desk.

So, you're ready to bring large language models into your business. That's a great move, but it takes more than just getting your hands on the tech. You need a smart, clear plan to turn that raw potential into real-world performance. The journey doesn't start with the technology—it starts with a hard look at your own business challenges and opportunities.

The first move is to identify high-impact use cases where an LLM can deliver quick, measurable wins. Don't try to boil the ocean. Instead, find a specific pain point. Could an LLM automate the first draft of weekly reports? Or maybe analyze customer feedback to spot recurring themes? What about powering an internal knowledge base for your support team? Nailing down a clear, achievable goal builds momentum and proves the concept early on.

Assembling Your Team and Data

With a target in sight, it's time to get your house in order. This is all about readiness—both with your people and your data. A successful LLM project needs a mix of skills: data science, engineering, and subject-matter experts who actually understand the business context. Building or bringing in that talent is a non-negotiable first step.

Just as critical is data readiness. The quality of your data is everything. An LLM's effectiveness is directly tied to what it learns from, so your internal data needs to be clean, organized, and accessible. For many businesses, this groundwork is the most important part of the whole process. If you want to dig deeper, our guide on how to implement AI in business has more detailed strategies.

An LLM is not a magic solution you simply switch on. It is a powerful engine that requires high-quality fuel—your data—and skilled operators—your team—to run effectively and deliver meaningful results.

Navigating the Build vs. Buy Decision

A huge fork in the road is deciding whether to build a custom model, buy access to a pre-built one, or find a middle ground. Building from scratch gives you total control but chews up massive resources and requires deep expertise. Buying access through an API is faster and cheaper, but you sacrifice customization and control over data privacy.

This decision often comes down to your security and compliance needs. It's telling that the on-premise deployment segment grabbed over 57.7% market share in 2023, a clear signal from industries like finance and healthcare that are extremely protective of their data.

There's also a powerful hybrid strategy called Retrieval-Augmented Generation (RAG). Think of it as connecting a general-purpose LLM to your company’s private, proprietary data. The model can then answer questions and generate text based on your internal knowledge base, giving you highly relevant results without having to be retrained from the ground up. This approach also boosts security by keeping your sensitive data separate from the core model.

By carefully mapping out these steps, you can create an LLM strategy that is scalable, secure, and perfectly aligned with your business goals.

Common Questions About Large Language Models

As leaders start digging into what large language models are, a few key questions pop up again and again. Getting these sorted out is the first step toward building an AI strategy that actually works. Most of the uncertainty boils down to practical things like cost, data security, and just getting the terminology straight.

Clear answers turn the conversation from "what if" to "what's next," making sure your organization is ready to move forward.

How Much Does It Cost to Implement an LLM?

There's no single price tag for using an LLM—the cost can swing wildly depending on how you go about it. Think of it as a spectrum of investment.

The most straightforward entry point is using a pre-built model from a major provider through an API. In this case, you're typically paying for usage, like how many words you process or generate. It’s a great, cost-effective way to get started.

Fine-tuning an open-source model is a bigger commitment. You'll have expenses for cloud computing resources and, just as importantly, the specialized talent needed to manage the entire process. Building a proprietary model from the ground up? That’s a multi-million dollar venture usually left to the biggest tech companies with massive resources.

The best way to begin is to start small. Find one specific, high-impact use case to prove the return on investment before you commit to a wider, more expensive rollout.

How Do We Ensure Our Company Data Stays Secure?

Data security is non-negotiable when you’re working with LLMs. The first rule is simple: never put sensitive or proprietary information into public-facing consumer tools, since that data could be used to train future models.

Instead, you need to look at enterprise-grade solutions that are built with security in mind. These often include:

Private Instances: A dedicated version of the model that only your organization can use.
On-Premise Deployment: Hosting the model on your own servers, giving you complete control over your data.
Virtual Private Cloud (VPC): A secure and isolated environment within a public cloud.

Always dig into a vendor’s data privacy policies and make sure they meet regulations like GDPR or HIPAA. Modern architectures like RAG also boost security by keeping your company’s data separate from the core model itself.

The demand for these kinds of secure, powerful tools is exploding. The global LLM market is expected to reach USD 9.98 billion in 2026 and jump to USD 24.92 billion by 2031. Looking further out, projections show it climbing to USD 823.93 billion by 2040, fueled by a 35.57% compound annual growth rate. You can discover more insights about this rapid market expansion and what it means for businesses.

What Is the Difference Between an LLM and Generative AI?

It’s easy to get these two terms tangled up, but the difference is actually pretty clear.

Think of Generative AI as the big umbrella. It’s the broad category for any artificial intelligence that can create brand-new content—whether that’s text, images, music, or code.

A Large Language Model is a specific type of generative AI. Its one and only job is to understand, process, and generate human-like text.

So, every LLM is a form of generative AI, but not all generative AI is an LLM. For example, an image generator like Midjourney is a powerful generative AI tool, but it's not a large language model.

Finding the expert talent to build and manage these powerful systems is the biggest hurdle for many organizations. DataTeams connects you with the top 1% of pre-vetted AI and data professionals, from Data Scientists to LLM specialists. Fill critical roles in as little as 72 hours and build the expert team you need to drive innovation. Find your next AI expert at https://datateams.ai.

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