Many organizations are using artificial intelligence (AI) powered by large language models (LLMs) in their day-to-day operations, relying on them to generate original content in human-like language. A global McKinsey & Co. study found that, in 2025, 88% of organizations regularly used AI for at least one business function, up 10% from the year before.
In recent years, generative AI—artificial intelligence that creates original content based on user prompts—has grown in scale and scope. LLMs are a type of generative AI that focus on processing, summarizing, and generating text and spoken language based on user prompts.
Defining Large Language Models
Large language models are artificial intelligence systems that respond to prompts by generating natural language text and other outputs. LLMs are trained on vast datasets and use machine learning techniques to find, process, and provide information. Some LLMs are proprietary to specific industries or organizations.
Proprietary Large Language Models
Proprietary LLMs are those controlled by private commercial organizations. While some are free to use, others require purchasing a license. Their code and architecture are the intellectual property of the companies that developed them. Well-known examples of proprietary LLMs include the following:
- Claude (Anthropic): Claude is an LLM that can be used to generate code, write content, and connect to Google workplace services.
- ChatGPT (OpenAI): ChatGPT can be used for research, content and image creation, and data illustration.
- Copilot (Microsoft): Copilot is an AI assistant meant to be used with Microsoft 365 to help with productivity and research.
Open-Source Large Language Models
Open-source LLMs are publicly available for anyone to use, modify, and distribute. Examples of open-source LLMs include:
- Llama (Meta): Llama is a group of pretrained LLMs offered free to the public for research and commercial purposes. The models are trained exclusively on publicly available datasets and intended to be used by academic researchers, policymakers, and industry professionals.
- DeepSeek-R1 (DeepSeek): DeepSeek-R1 is an advanced reasoning model capable of complex math, coding, and logical reasoning.
- Falcon LLM (Technology Innovation Institute): Falcon LLM is a family of LLMs focused on translation, generating text, answering questions, and generating code.
Types of Large Language Models
An LLM’s abilities depend on how its developers trained it and its intended use.
Base Models
Base models are the foundation of LLMs before specialized training. They use pattern recognition to predict the next word in a sequence and usually lack the ability to follow specific instructions reliably. Several LLMs can be built from the same base model, depending on the specialized training the LLM requires.
Instruction-Tuned Models
Instruction-tuned models are trained with supervised learning and human feedback. Because of their training, they are capable of understanding and executing on users’ commands. This makes them useful for task-oriented applications.
Reasoning Models
Reasoning models are explicitly designed for complex cognitive tasks. They return results that show their intermediate steps, providing transparency for users. These models are used for multistep thought processes because they show their processes and allow for adjustments at specific steps.
Mixture of Experts Models
Mixture of experts (MOE) models use specialized subnetworks and gating mechanisms to route tasks. This allows for more parameters with better computational efficiency. Because only relevant sources are used to process inputs, MOEs use fewer resources. Their ability to break tasks into smaller segments also allows them to take on complex tasks.
Multimodal Models
Multimodal models are used to process images, audio, and video along with text. They work by embedding alignment and fusion to make connections between encoders. These models require large parameter counts, massive datasets, and fusion mechanisms.
Hybrid Models
Hybrid models combine symbolic reasoning with pattern-matching neural networks. They can use different approaches for each step of a complex task. This makes them more flexible and allows for more uses.
Deep Research Agents
Deep research agents use LLMs, the internet, and interactive refinement to conduct multistep research tasks. They can assist with sustained investigations that synthesize massive amounts of information from many sources.
Resources for Understanding Different Types of LLMs
Given the speed at which AI continues to advance, keeping up with the different kinds of large language models and what they are capable of can be challenging. These resources offer a taxonomy of LLMs to help users understand the options.
How Does a Large Language Model Work?
A large language model is a complex system that requires layers of training, machine learning, and fine-tuning. The more data an LLM is trained on, the better it becomes at generating correct, relevant, and useful answers and information.
Training an LLM
Before an LLM can be used, developers must train it by inputting data. Each model and specialization is trained in a specific way to make it most useful for its intended purpose.
Data Collection
Developers choose training data based on the LLM’s intended end use. Training data can be domain-specific (limited to a specific subject area), drawn from an organization’s internal proprietary databases, or taken from sources such as conversation logs or web archives such as Common Crawl.
Developers expose LLMs to massive amounts of textual data that teaches them to understand context, grammar, information, and language patterns and provides them with information to pull from when they are queried. This data is stored in a NoSQL database after it is cleaned, processed, and standardized.
Supervised Learning
In supervised learning, labeled datasets are used to train LLMs. This allows the trainers to provide the LLM with standard metrics, domain-specific information, and examples of contextual relationships. Supervised learning is used in cases of limited training time, or when the model is being asked to do specific tasks based on predictable variables.
Unsupervised Learning
In unsupervised learning, unlabeled datasets are used to train LLMs. Developers enter raw data into the LLM to teach it to group the data based on inherent patterns. Trainers use unsupervised algorithms to give the LLM tasks such as clustering data groups, identifying if-then patterns, and turning complex data into simplified outputs.
Using an LLM
When a user inputs information into an LLM, several steps happen before a response is generated. The first step is tokenization. Tokens are basic units of text such as words, parts of words, or characters. The LLM turns the user’s prompts into streams of tokens.
Once the tokens have been created, the LLM turns them into numbers, using positional encoding to add context. The prompt becomes a series of numbers known as a vector. Next, the LLM uses transformers to generate text one token at a time, sending a response back to the user. It calculates the probability of all the possible next tokens and responds with the most likely one, making its decisions using statistical relationships it learned during its training.
How Are Large Language Models Used?
Different industries and types of organizations use large language models for different purposes. For example, retail businesses can use LLM-powered chatbots to deliver product descriptions to customers and to answer their questions.
Text Generation
LLMs can create blog posts, articles, legal memos, or any other type of content. Users input prompts describing what they want, and LLMs return completed content. Because LLMs can fall prey to biases and errors known as hallucinations, their output should always be considered a first draft. Humans should always review all AI-generated content.
Text Summarization
LLMs can generate synopses of input text in a variety of formats. The output depends on how the user asked for the information to be presented.
AI Assistants
LLM-powered AI assistants can answer questions, update schedules, and perform other routine tasks. They can also be used as real-time customer-facing chatbots.
Code Generation
LLMS are useful in generating new computer code and finding mistakes in existing code. Programmers can use them to develop the code for websites, apps, and other projects and can detect any cybersecurity issues with the code.
Language Translation
Many LLMs can perform real-time language translation. Users can employ LLMs to translate between many languages to improve their communication with others.
Reasoning
Users can input math problems of varying complexity and LLMs can provide solutions to them. They can also plan multistep processes and simplify difficult procedures.
Getting the Most out of Large Language Models
LLMs are powerful tools, but learning the most effective ways to use them can help users increase the quality of the outputs they receive. These tips can help users get the most useful results from LLMs.
- Craft strong prompts: Be specific, stating the exact goals, context, and target audience of the response, rather than asking general questions.
- Avoid ambiguity: Vague inputs lead to vague outputs.
- Specify formatting: Tell the LLM how to present its results in the most useful form.
- Break down steps: Split complex problems into smaller questions.
- Compare results: Prompt different LLMs to respond to the same query to discover which one delivers the most valuable responses.
LLM Usage Resources
Understanding how LLMs work on a deeper level allows users to employ them more efficiently. These resources provide further information.
Large Language Models and the Future of Work
Large language models have many uses, and they are becoming increasingly central to modern business and academic research. While there are still concerns about ethics and reliability, the training of LLMs is improving over time and many industries and organizations are establishing ethical guidelines for their use. Users who understand how LLMs work are more likely to have positive results.