What are Large Language Models (LLMs)? Examples Included

Large language models (LLMs) understand and generate human-like text. They learn from vast amounts of data and identify patterns in language, enabling them to understand the context and produce outcomes based on that information. You can use LLM software to write text, personalize messaging, or automate customer interactions.

Many businesses turn to artificial intelligence (AI) chatbots based on LLMs to automate real-time customer support. However, even with their advantages, LLMs don’t come without challenges; they have some drawbacks.

This article takes a look at various use cases of LLMs, along with their benefits and current limitations.

Why are large language models important?

LLMs can perform several tasks, including answering questions, summarizing text, translating languages, and writing code. They’re flexible enough to transform how we create content and search for things online. 

They may occasionally produce errors in output, but this usually depends on their training. 

Large language models are generally trained on internet-sized datasets and can perform multiple tasks with human-like creativity. Although these models aren’t perfect yet, they’re good enough to generate human-like content, amping up the productivity of many online creators. 

How do LLMs work?

Previous machine-learning models used numerical tables to represent words. However, they had yet to recognize relationships between words with similar meanings. For present-day LLMs, multi-dimensional vectors, or word embeddings, help overcome that limitation. Now words with the same contextual meaning are close to each other in the vector space. 

LLM encoders can understand the context behind words with similar meanings using word embeddings. Then, they apply their language knowledge with a decoder to generate unique outputs.

Full transformers have an encoder and a decoder. The former converts input into an intermediate representation, and the latter transforms the input into useful text.

Several transformer blocks make a transformer. They include layers such as self-attention, feed-forward, and normalization layers. They work together to understand the context of an input to predict the output. 

Transformers rely heavily on positional encoding and self-attention. Positional encoding allows words to be fed in a non-sequential fashion. It embeds the input order within a sentence. Self-attention assigns weights to every piece of data, such as the numbers of a birthday, to understand its relevance and relationship with other words. This provides context. 

As neural networks analyze volumes of data, they become more proficient at understanding the significance of inputs. For instance, pronouns like “it” are often ambiguous as they can relate to different nouns. In such cases, the model determines relevance based on words close to the pronoun.

Timeline of major LLM releases (2023- mid 2025)

Source: ResearchGate

How are large language models trained?

Large language models use unsupervised learning for training to recognize patterns in unlabelled datasets. They undergo rigorous training with large textual datasets from GitHub, Wikipedia, and other informative, popular sites to understand relationships between words so they can produce desirable outputs, all powered by the generative AI infrastructure software that enables these massive models to train and scale efficiently.

They don’t need further training for specific tasks. These kinds of models are called foundation models. 

Foundation models use zero-shot learning. Simply put, they don’t require much instruction to generate text for diverse purposes. Other variations are one-shot or few-shot learning. They all improve output quality for selective purposes when they’re fed with examples of correctly accomplishing tasks.

What are some large language model examples?

Large language models generally fall into three architecture categories. Understanding these classes makes it easier to see why some models excel at classification or retrieval, while others are designed for generating long-form content or powering assistants. 

• Encoder-only is suitable for tasks that involve understanding language to perform classification or sentiment analysis. Bidirectional Encoder Representation from Transformers (BERT) is a popular example of an encoder-only LLM class.
• Decoder-only models are designed primarily for generation. These models predict the next token in a sequence, making them strong at writing, summarizing, reasoning through problems, and generating code. Most of today’s frontier “assistant models” (including GPT and Claude families) are decoder-based. Some AI writing tools are based on this class of LLMs.
• Encoder-decoder models combine both approaches and are commonly used for structured transformation tasks, such as translation, summarization, and instruction-driven generation. T5 is a well-known example of this design approach. 

Some notable LLM model examples

Here’s a snapshot of prominent LLMs in use as of 2026, spanning commercial, open-source, and specialized deployments. You can compare the top LLM tools on G2 and compare them based on real user reviews.

• GPT-5.2 (OpenAI): OpenAI’s flagship frontier model family, optimized for long-context reasoning, agent workflows, and real-world business use cases.
• GPT-4o (OpenAI): Multimodal “omni” model capable of real-time reasoning across text, audio, and vision.
• Claude Sonnet 4.5 (Anthropic): High-performance hybrid reasoning model with strong coding, agentic, and long-document capabilities.
• Claude Opus 4.5 (Anthropic): Anthropic’s most powerful model, designed for complex reasoning and long-form generation at scale.
• Gemini 3 Pro (Google): Google’s top Gemini model, focused on multimodal reasoning, agentic behavior, and enterprise deployment.
• Gemini 3 Flash (Google): Lower-latency, cost-efficient Gemini model built for speed and large-scale production use.
• Llama 4 (Scout / Maverick) (Meta): Meta’s natively multimodal, open-weight Llama 4 models, widely used for fine-tuning and self-hosted deployments.
• Mistral Large 3 (Mistral AI): Mistral’s most capable open-weight generalist model, competing directly with closed frontier models.
• Command A (Cohere): Cohere’s leading enterprise LLM, optimized for retrieval-augmented generation (RAG), tools, and business workflows.
• DeepSeek-R1 (DeepSeek): Open, reasoning-focused model trained with large-scale reinforcement learning, widely adopted for research and cost-efficient reasoning

What’s the difference between LLM and generative AI?

Large language models are a specialized subset of generative AI, focused on language-based reasoning, analysis, and content generation. Generative AI more broadly refers to systems capable of creating various types of content, ranging from text and images to music, video, and 3D assets.

Generative AI platforms, such as Sora (text-to-video), DALL·E 3 (image generation), and Runway (video editing), are designed to handle non-linguistic data formats and produce creative outputs. Their primary use cases span digital content creation, marketing, gaming, and design.

In contrast, LLMs are optimized for natural language understanding and generation. Recent models like GPT-5, GPT-5.1, GPT-5.2, Claude Opus 4.5, Gemini 3, and Gemini 3 Pro push the limits of what LLMs can achieve. These systems offer multimodal input support, extended context windows, improved alignment with user intent, and enhanced performance on reasoning tasks. While rooted in text, many now support images and audio as part of their input and output pipelines, bridging the gap between LLMs and broader generative AI.

What are the applications of large language models?

Large language models have made various business functions more efficient. Whether for marketers, engineers, or customer support, LLMs have something for everyone. Let’s see how people across industries are using it.

Customer support

Customer support teams use LLMs that are based on customer data and sector-specific information. It lets agents focus on critical client issues, while engaging and supporting customers in real time.

Marketing

Sales and marketing professionals personalize or even translate their communication using  LLM applications based on audience demographics. 

Encoder-only LLMs are proficient in understanding customer sentiment. Sales teams can use them to hyper-personalize messages for the target audience and automate email writing to expedite follow-ups. 

Some LLM applications allow businesses to record and summarize conferencing calls to gain context faster than manually viewing or listening to the entire meeting. 

Product development and research

LLMs make it easier for researchers to retrieve collective knowledge stored across several repositories. They can use language learning models for various activities, like hypothesis testing or predictive modeling, to improve their outcomes.

With the rise of multimodal LLMs, product researchers can easily visualize design and make optimizations as required. 

Risk management and cybersecurity

Enterprises cannot do away with compliance in the modern market. LLMs help you proactively identify different types of risk and set mitigation strategies to protect your systems and networks against cyber attacks.

There’s no need to tackle paperwork related to risk assessment. LLMs do the heavy lifting of identifying anomalies or malicious patterns. Then, they warn compliance officers about the sketchy behavior and potential vulnerabilities.

On the cybersecurity side, LLMs simulate anomalies to train fraud detection systems. When these systems notice suspicious behavior, they instantly alert the concerned party. 

Supply chain management

With LLMs, supply chain managers can predict growing market demands, find good vendors, and analyze their spending to understand supplier performance. This gives a sign of increased supply. Generative AI helps these professionals 

Multimodal LLMs examine inventory and present their findings in text, audio, or visual formats. Users can easily create graphs and narratives with the capabilities of this large language model.

What are the benefits of large language models?

Large language models offer several advantages on a variety of fronts.

• Improve continuously. The more LLMs learn, the better they become. After pretraining, you can use a few-shot prompting to help the model learn from inputs and produce more desirable outputs. 
• Don’t require many examples. LLMs learn quickly because they don’t need additional weight, resources, or training parameters. 
• Allow non-technical users to automate monotonous tasks. LLMs can understand human language. Professionals can engineer their prompts in human language to set expectations for LLMs. They can use it to automate labor-intensive tasks. 
• Enable translation. LLMs learn different language structures through recurrent neural networks. This allows for easy cross-cultural communication and lets users personalize interactions in their customers’ local language. 
• Create summaries and deliver insights. You can quickly input comprehensive text or data, and LLMs grasp context through summaries and analysis.

What are the limitations of large language models?

Large language models solve many business problems, but they may also pose some of their own challenges. As LLMs become more advanced, distinguishing human-written and AI-generated content can be difficult, which is why AI content detectors are becoming increasingly essential for maintaining transparency and trust online. The other challenges with LLMs include: 

• Require large datasets to train. Companies that intend to develop LLMs often struggle to get their hands on large enough datasets to effectively train their model. 

• Need niche technical experience. To develop LLMs, businesses need engineers and architects with a remarkable understanding of deep learning workflows and transform networks.

• Can make mistakes. If they’re trained on biased data, LLMs can produce biased outputs. They might even raise unethical or misleading content. 
• Have to have robust privacy measures. Large language models can struggle with data privacy, as working with sensitive information is tricky. 
• Are susceptible to hackers. Some malicious users design prompts to disrupt an LLM's functionality. These are known as glitch tokens, and you need strong security to protect yourself against them.

Frequently asked questions about large language models

Got more questions? We have the answers.

Q1. Can businesses use LLMs safely?

Yes, but implementation requires careful attention to data privacy, alignment, and security. Enterprise-grade LLMs often include safeguards, moderation tools, and audit capabilities.

Q2. How do LLMs differ from traditional AI models?

Traditional AI models are often rule-based or trained for narrow tasks. LLMs, on the other hand, are pretrained on massive datasets and can generalize across a wide range of language tasks without retraining.

Q3. What industries benefit most from LLMs?

LLMs are transforming industries such as customer service, marketing, legal, healthcare, finance, and education, especially in areas that involve language comprehension, automation, or customization.

Q4. What’s the role of fine-tuning in LLM performance?

Fine-tuning adapts a general-purpose LLM to perform better on domain-specific tasks, such as legal analysis, medical documentation, or customer support workflows.

Q5. Are open-source LLMs a viable alternative to proprietary models?

Yes. Open-source LLM models offer strong performance for organizations that want more control over deployment, customization, and cost.

Q6. What should enterprises look for when selecting an LLM?

Key considerations include model size, accuracy, latency, modality support (text, image, audio), license terms, integration capabilities, and safety features.

Toward improved accuracy

As LLMs train on high-quality datasets, the outcomes you see will continue to improve in accuracy and authenticity. The day is not far off when they can independently solve tasks to achieve desired business outcomes. The speculation about how these models will impact the job market has been a hot debate for some time now.

But it’s too early to predict. LLMs are certainly part of many business workflows these days, but whether they will replace humans is still debatable. 

Learn more about unsupervised learning to understand the training mechanism behind LLMs.

This article was originally published in 2024. It has been updated with new information.