What are Large Language Models (LLMs)?

Artificial intelligence (AI) is transforming industries but cutting through the buzz can be difficult — especially with the profound breakthrough of large language models (LLMs). Frequently described as paradigm-shifting technologies, akin to the impact of electricity, LLMs are powerful tools for understanding and generating language-based outputs. While their complexity and broad potential can feel overwhelming, LLMs become easier to grasp once you understand what they and their core capabilities. Here, we explain LLMs, why they’re crucial for modern companies and how they fit into the broader AI landscape, making it easier to see their real-world value.

Large Language Models Explained

What are Large Language Models?
What are the Features of Large Language Models?
How do Large Language Models Work?
Why are Large Language Models Important?
What are Large Language Model Use Cases?
How are Large Language Models Priced?
Large Language Model Companies

What are Large Language Models?

Large language models are sophisticated AI systems specifically designed to process and generate human-like text. While they build on the pattern recognition capabilities of deep learning, LLMs are uniquely effective at processing and generating natural language at unprecedented scale and quality, according to AWS. Interacting with LLMs often involves prompting, where a human inputs natural language, and the LLM responds with a relevant and human-sounding response.

History of Large Language Models

The development of large language models is rooted in early natural language processing (NLP) techniques, but major advancements have accelerated in the last decade. In 2013, Google introduced Word2Vec, a model that represented words as vectors in a high-dimensional space, allowing LLMs to understand semantic relationships between atomic language units called tokens. Tokens can be words or pieces of words that are contextually dependent. This, and similar techniques, known as embeddings, make it possible to apply mathematical approaches at scale across all languages.

In 2017, an eight-person team from Google Brain published “Attention is All You Need,” a paper introducing the Transformer model. The Transformer architecture was a significant breakthrough, enabling models to process and generate text more efficiently by focusing on “attention mechanisms.” Unlike earlier models’ approaches, which struggled with longer sequences, the Transformer could capture context over extended passages of text and, in turn, generate sequences. These generated sequences predict the next token, which has made transformer-based NLP approaches the most human-sounding and the cornerstone of modern AI investment.

Since 2017, scale and multimodality have been significant advances. The new generation of LLMs, including OpenAI’s GPT series, Google’s Gemini, Anthropic’s Claude and Meta’s Llama. All utilize billions of parameters across various data sources to achieve high-quality text, video and image generation and comprehension. Consistent improvement has been seen the more tokens and compute a model developer gives during model training. These models have since been adapted for various applications, from customer service to content creation, making language models one of the most rapidly advancing areas in AI.

What are the Features of Large Language Models?

Given their extensive training corpus and compute resources, advanced LLMs learn complex language patterns, context and even subtle nuances in communication. As a result, they can handle a wide range of tasks without needing task-specific programming. Their success rate can improve depending on the additional context that they have either in their training data or with the prompt. Refining prompts to achieve better results is called prompt engineering. The nuances of a particular prompt, or chain of prompts, can create significant differences in model outputs.

The features of LLMs are based on their understanding of relationships between tokens. This results in an ability to predict the next token, which can then be used to determine benchmarks across use cases. Some of the more recent models are controversially called “reasoning models,” but this same, consistent next-token approach is the architecture underlying their performance.

Key Features of LLMs

Contextual understanding: LLMs interpret text based on context, capturing nuance, intent and relationships between concepts. This allows them to provide accurate, relevant responses to complex or ambiguous queries — far beyond simple keyword matching.
Embeddings for semantic representation: LLMs use embeddings to represent words and phrases as vectors in high-dimensional space. This enables the model to recognize semantic relationships, handle synonyms and understand contextual variations, forming the backbone of their ability to interpret language meaningfully.
Retrieval-augmented generation (RAG): RAG combines LLMs with real-time access to external databases, improving factual accuracy and reducing errors or hallucinations. This is especially useful in applications requiring up-to-date or domain-specific knowledge.
Natural language generation: LLMs can produce coherent, human-like text across formats — from brief answers to in-depth reports — adapting to different tones, styles and formats based on the data they were trained on.
Few-shot and zero-shot learning: With few-shot and zero-shot learning capabilities, LLMs can adapt to new tasks with minimal or no examples, making them highly versatile across diverse applications without extensive retraining.
Scalability and high-volume processing: LLMs can process large volumes of text data efficiently, automating language-heavy tasks that would be impractical for human teams, such as large-scale content generation and summarization.
Memory and context retention: Newer LLMs retain context over longer sequences, supporting coherent responses in extended conversations and document-based tasks, which is crucial for applications requiring continuity.

How do Large Language Models Work?

Large language models work by predicting the most likely next token based on patterns they’ve learned during training. This enables them to handle a wide variety of language-based tasks using methods like self-supervised learning and reinforcement learning.

The core of an LLM’s functionality lies in its training process. These models are trained on massive data sets containing billions of words, sentences and documents — sourced from books, websites, research articles and other text-heavy repositories. Through self-supervised learning, the model essentially reads this text, learning patterns in grammar, syntax and context by predicting missing words or phrases. This helps it understand how language is structured and how ideas connect.

The Transformer model relies on a mechanism called attention. Attention allows the model to weigh the importance of different words in a sentence or paragraph, enabling it to grasp context over long stretches of text — something earlier models struggled with. This mechanism is why LLMs can generate coherent, contextually relevant responses in real-time, even when responding to complex or nuanced prompts.

This video with Andrej Karpathy, an educator in the LLM space, provides additional LLM details.

Key LLM Terms

Embeddings: Embeddings are numerical representations of words or phrases. LLMs map words into a high-dimensional space, allowing them to understand the relationships between words (e.g., cat and feline will be closer in this space than cat and table).
Weights: Weights are the adjustable parameters within the model that influence how it processes language. These dials are fine-tuned during training to help the model interpret and generate text more accurately. Generally, the more parameters, or weights, a model has, the more nuanced and accurate its understanding of language becomes. However, some models can achieve comparable performance with fewer parameters by using optimized algorithms or more efficient training techniques.
Context memory: LLMs can remember the context of previous interactions over a long text sequence, allowing them to provide more coherent and relevant responses in longer conversations or documents.
Prompt engineering: The quality of an LLM's output depends largely on the input prompt. By carefully structuring and refining prompts, users can guide the model to produce more accurate and useful responses.
Tokens: LLMs don't process whole words but break text down into smaller units called tokens, which can be as short as a character or as long as a word. The model learns to predict the next token in a sequence, enabling it to generate text. Often the more tokens in a model, the more accurate.
Inference: Inference refers to the process of the model generating real-time responses based on new input. This is what happens when an LLM is deployed in applications, like chatbots or content generation tools.

This video by IBM offers an additional explanation of how LLM technology works:

See more: Thinking of Building an LLM? You Might Need a Vector Database

Why are Large Language Models Important?

Large language models are transforming how businesses operate by automating and enhancing a wide range of language-driven tasks, including:

Advanced automation: LLMs bring near-human quality to automated tasks, like customer support, content generation and data analysis. This allows businesses to scale their operations while reducing dependency on manual effort, freeing up human resources for more strategic work.
Unlocking insights from unstructured data: Businesses generate massive amounts of unstructured text data — emails, customer reviews, support tickets and social media posts. LLMs can sift through this data, extracting patterns and insights that would be impossible or prohibitively expensive to capture manually, enabling data-driven decision-making at scale.
Enhanced personalization: By tailoring responses and content to individual customers, LLMs elevate customer engagement and loyalty. From personalized product recommendations to custom messaging in marketing, LLMs help brands create experiences that resonate with each customer’s unique preferences and needs.
Accelerating content production: Whether it’s generating marketing copy, technical documentation or product descriptions, LLMs speed up content creation processes. This means faster time to market for campaigns, improved SEO with fresh content and streamlined workflows for content teams.
Supporting better decisions: LLMs summarize and synthesize large volumes of information, helping teams stay on top of market trends, customer sentiment and industry developments. By turning data into actionable insights, LLMs empower leaders to make informed, data-backed decisions faster.

Companies are also increasingly giving employees access to enterprise chatbots. Beyond broad operational impacts, AI can be a “thinking companion” for staff, says Ethan Mollick, an AI commentator at the Wharton School of Business. This cognitive support can help foster bottom-up creativity and innovation. LLMs are enabling businesses to operate more efficiently, engage customers more effectively and make smarter decisions.

What are Large Language Model Use Cases?

Large language models are transforming business operations across industries. Common use cases include:

Customer service: AI-powered chatbots and virtual assistants respond to customer inquiries in real-time, providing human-like interactions that reduce the need for human agents. For example, Klarna's AI reportedly performs the work of 700 customer service representatives, allowing the company to serve more customers with fewer resources.
Content creation: LLMs can generate marketing copy, product descriptions and personalized emails, streamlining workflows.
Market research: LLMs summarize large volumes of customer feedback, social media data and industry reports, helping businesses extract actionable insights
Legal and compliance: LLMs can analyze legal documents and contracts, identifying key clauses and risks and ensuring regulatory compliance
Human resources: LLMs can optimize recruitment by screening resumes, generating job descriptions and conducting initial candidate evaluations.

Common Considerations for LLM Usage

As with any tool, LLMs are best used in the context of trying to solve a specific problem. Beyond these specific role use cases, evaluating the usage of LLM should consider the following:

Data quality and privacy: LLMs rely on high-quality data to perform well. Ensure your data is accurate and representative and protect sensitive information — especially in regulated industries, like health care and banking (e.g., customer support chat, and patient records).
Interpretability: LLMs can operate as black boxes, meaning their internal processes aren’t easily explainable. If transparency is critical — such as in legal or compliance work — consider whether the lack of interpretability could be a limitation.
Task complexity and scale: LLMs are ideal for tasks involving large volumes of unstructured text, such as content generation and sentiment analysis. For simpler tasks, rule-based systems or smaller models may be more efficient (e.g., basic customer FAQ responses).
Latency and cost: Running large LLMs in real-time can be costly and may introduce delays. For time-sensitive applications, like customer service, evaluate if the latency and expense are justified by the model’s value.
Customization needs: If your application requires specific industry knowledge or jargon, fine-tuning may be necessary. Some LLMs support customization, but this process requires resources and expertise (e.g., adapting for legal or medical terminology).
Ethics and bias: LLMs learn from vast data sets that may include biased language. This can lead to outputs that reinforce stereotypes or produce unintended harm. Assess potential risks and consider post-processing or filtering where possible (e.g., ensuring unbiased job descriptions).

How are Large Language Models Priced?

Large language model pricing varies depending on usage needs, deployment method and customization requirements. Here are the main pricing structures:
Subscription-based platforms: For out-of-the-box conversational AI solutions, like chatbots, many providers offer subscription plans, with pricing based on the number of users, conversation volume or desired features. Enterprise plans typically include added security, compliance and support.

API access and usage-based fees: For developers integrating LLMs into custom applications, providers often offer API access priced by usage volume, typically per thousand or million tokens processed. This pay-as-you-go model allows companies to scale their usage flexibly, though costs can increase quickly for high-traffic applications. For an idea of pricing, here’s a comparison by AgentOps.
Customization and fine-tuning costs: Fine-tuning an existing model on specific data to improve accuracy for specialized tasks (e.g., legal or medical applications) incurs additional fees, ranging from a few dollars to thousands of dollars. This is significantly more affordable than training a model from scratch, making it a popular option for businesses needing domain-specific adaptations.
Training from scratch: Building a large language model from the ground up can cost millions due to the high computational requirements and extensive data needs. This option is typically limited to organizations with specialized requirements and significant resources. For a sense of costs, OpenAI offers this as a solution with a starting cost of $2-$3 million.

In general, LLM pricing offers flexibility, allowing businesses to select the model and deployment approach that aligns with their budget, scalability needs and customization preferences.

Large Language Model Companies

Several companies are driving innovation in the LLM space, with there being centralized players and increasingly diverse multi-modal companies. The main players include:

OpenAI: Known for its GPT models and provides API access and the ability to create custom versions of the model for particular purposes
Anthropic: Prioritizing safety through “Constitutional AI,” Anthropic offers Claude LLM
Google: Builder of the Gemini models, for enterprise-ready language applications
Hugging Face: Provides a platform for accessing, training and fine-tuning various open-source LLMs.
Cohere: Specializes in language models for business applications, with a focus on enterprise needs.
Meta: A leading provider of advanced and mostly open-source models with its Llama line
Microsoft: Offers enterprise-ready LLM solutions integrated with its Azure cloud services
AWS: Hosts various LLMs through the Bedrock platform on AWS
NVIDIA: Powers LLM training and deployment with its GPUs and the NeMo framework, supporting large-scale model customization and optimization
Midjourney: Generates high-quality images from text prompts, popular for creative and marketing teams
Runway: Provides generative video tools, allowing users to create and edit video content with AI
Eleven Labs: Specializes in AI-generated voice synthesis for creating realistic voiceovers