LLM (large language model) might not be the catchiest name. But it’s at the heart of the wave of AI-centric technology washing across every industry and most parts of the world. After all, the AI tools that most consumers have tried are powered by LLMs.

It’s the ability to effectively parse, analyze, and generate human language that makes LLMs so impactful. AI-enhanced tools aren’t new, but achieving the level of accessibility that comes with language-based instruction is a game-changer.

This article will look to demystify LLMs and clarify what they offer. We’ll provide a large language model definition, explain why LLMs are so important, and how you can use them. We’ll delve into the technology that makes them work, explain concepts including tokenization and transformer models, consider the challenges of implementing LLMs, and look ahead to their future.

What is a large language model (LLM)?

LLMs are a form of artificial intelligence built on deep machine learning using neural networks, particularly leveraging advanced architectures. One notable mechanism is the transformer architecture, which employs attention mechanisms to focus on different parts of the input data dynamically, enabling the model to capture contextual relationships effectively. For instance, the self-attention mechanism within transformers allows the model to understand dependencies between words in a sentence, regardless of their position.

Another example is feed-forward networks, integrated within transformer layers, which process information at each step of the sequence to refine and produce intermediate representations. These mechanisms, combined with large-scale training datasets, make neural networks highly effective for tasks such as language translation, summarisation, and text generation.

Think of the human brain as a computer of its own: this computer processes text as a collection of words, with sentences, paragraphs, documents, and so on as word sequences. These sequences come with rules, such as grammar, context, and cultural nuances, which allow us to understand and use the right words in the right order.

To the machine, though, that text is just a sequence of non-contextual characters. Through training and fine-tuning, LLMs can process the user’s text-based input by converting text into “tokens,” processing those “tokens” with respect to the context in the text and the wider context of the training data, and then providing an answer in a particular format.

Why are large language models important?

Compared to previous attempts at AI, large language models (LLMs) are important because they get closer to the vision of human-like AI systems. Ones that can interact with humans in natural ways. These models have already given rise to advanced AI assistants capable of fielding tough queries and effectively integrating them with existing systems.

Taking advantage of machine learning was previously limited to those with technical skills or enough money to pay for said skills. LLMs point to a future of widespread access to AI tools.

Given the incredible scope and scalability of these models, LLMs also hold the potential for breakthroughs in vital areas such as healthcare. Machine learning can help researchers identify patterns in vast datasets and test countless scenarios to discover innovative treatments (many of which would never be found through manual effort alone).

Furthermore, doctors equipped with LLMs trained in everything medical science can use their time more efficiently and potentially diagnose symptoms more accurately.

The benefits: Why use a large language model?

It’s worth noting that there are different types of LLMs out there. One of the more prominent LLM types is generative AI. Most notably, the generative model that is designed to generate human-like, conversational language. But there are many others. For example, Cohere offers three different types of LLM: along with the generative model Cohere Command, there is also the embeddings model Cohere Embed, used for semantic search, among other things, and Cohere Rerank, built to help improve the search relevance of any keyword or vector search system. Individually or chained, these models can be very powerful tools for business.

The potential use cases and related efficiency gains of using one or several LLMs are very much top of mind for global business leaders: 98% of global executives agree that AI models will play an important role in their organization’s strategies in the next three to five years.

Generative AI could boost global productivity by around 7% annually over 10 years, considering efficiency gains and new categories of jobs. These transformative gains in productivity can also add value and impact the bottom line: we’ve previously reported that the revenue growth gap between early adopters and laggards is growing and expected to increase 2.4x by 2026. Perhaps that’s why McKinsey found that 40% of business leaders expect to increase their investment in AI overall because of advances in generative AI.

Examples of large language models and use cases

Large language models are popping up all over the business world, and they are being used to power many different processes because they are incredibly versatile. Here are just a few examples of business solutions powered by LLMs in the wild.

AI assistants

AI assistants are what brought LLMs into the mainstream, making the models accessible to everyday users. They’re seen a lot in customer service, holding ongoing conversations to help customers troubleshoot or get information. However, they can be deployed across sectors, including regulated industries such as commercial insurance.

More robust search

Whether it’s improving legacy search systems with Rerank or implementing semantic search with embeddings, LLMs are propelling new and improved search systems that power many tech solutions in the market today.

Text summarization

Seen by some as a critical natural language processing (NLP) task, text summarization is a growing area for LLMs. It’s a great use case, too, given LLMs’ proficiency in synthesizing large volumes of text and our own drive for efficiency and productivity. For example, an LLM can take meeting transcripts and give you the high-level action points in an instant.

Sentiment analysis

You can use LLMs to not only interpret data but also to analyze and classify the sentiment of that data. They are most often deployed in customer service and marketing to extract insights into how customers are talking about a brand or product. Sentiment analysis has been around for a long time, but the latest generation of LLMs has heightened its accuracy, granularity, and efficiency.

Procurement management

Adding efficiency and robustness to enterprise knowledge, LLMs with advanced multi-hop tools are used to summarize historical vendor procurement documents and optimize contract terms. These lengthy procurement processes can result in multiple documents from each potential supplier, and extracting the right information can be time-consuming. LLMs can ingest and summarize multi-modal files — such as PowerPoint decks, notes, and PDF documents — and the model is trained to use previous contract negotiation materials to help procurement staff better understand history, informing current and future negotiations.

The scalable model: LLMs optimized for RAG

Advances in AI are building on current models to develop new capabilities. For example, retrieval-augmented generation (RAG) can provide LLMs with access to up-to-date, real-time information and domain-specific knowledge. RAG helps to improve the reliability and explainability of generative AI while accessing proprietary data stores.

This technology enables complex workflows like code generation, tool use, and AI agents. With LLMs optimized for RAG, enterprise users can “move into production at scale with a balance of strong accuracy and high efficiency designed for enterprise,” says Greg Pavlik, SVP at Oracle AI.

With or without large context windows, LLMs with RAG quickly become a must-have for businesses, especially when it comes to efficiency, performance, accuracy, and flexibility.

Enter the era of production-ready, RAG-supported business applications: report and meeting summaries, efficient retrieval of insights from internal documentation, customer success management using sales comments, talent management using RAG for employee knowledge training, automations and independent AI agents and more. For more, check out our R series of Command models.

The journey to large language models

So, how did we get here? While the uninitiated may use the terms artificial intelligence, machine learning, deep learning, and neural networks interchangeably, they are better thought of as a “series of AI systems from largest to smallest, each encompassing the next,” says IBM.

Machine learning is a subset of AI, with deep learning a subset of that, and neural networks as the “backbone” of deep learning algorithms. Let’s look at the journey so far.

Large language models explained: How do LLMs work?

To tap the potential of LLMs, enterprise leaders need to learn the lingo. Let’s run through some of the key concepts at a high level.

To start, understand that LLMs are built in distinct parts: the user’s input runs through tokenization, then embedding, then positional encoding, and is fed through transformer blocks that use the attention mechanism to process data before finally delivering output as an answer to the user. We’ll go into each of these more below for text-based generative AI. Remember: it’s all about helping the model to understand context, which helps it predict the next word in a sentence with greater precision.

Tokenization

The text of the document, including punctuation signs, prefixes, and suffixes – every little bit of written language – is turned into a stream of tokens. These tokens each typically correspond to 3-4 characters. It is easier for us humans to think about each token corresponding to an entire word, although very few modern tokenizers actually do this.

Embeddings and positional encoding

Then, it’s time to turn the tokens into numbers using a process known as embedding. It’s like assigning scores and properties to words used by humans so that the LLM can understand, translate, and answer. To help the model understand context, these embeddings need to capture similarities between words within sentences, which is where positional encoding comes in.

By assigning multiple properties to words — taking in properties such as age, gender, size, type, and more — each word becomes associated with a vector(row of numbers), which helps the LLM to understand the context of both the word and how it’s used.

In general, embeddings send every word (token) to a long fixed-length list of numbers called vectors.

Transformer blocks and the attention mechanism

This stream of vectors then flows through to the transformers. Once words of a sentence have been input, turned into tokens, then into numbers via embeddings, and then positionally encoded to add context, we’ll have a vector for every token input to the model.

What happens next? It’s time for a super-large neural network that’s been trained precisely to generate the next steps, and it does this with the help of the attention mechanism — a way to tell words apart when used in different contexts. It’s an idea explored in the seminal paper Attention is All You Need, which was co-authored by Cohere Co-Founder and CEO Aidan Gomez.

Humans naturally use neighboring words to understand context, which helps them to understand whether “bank” means a riverbank or a financial institution. The attention mechanism of an LLM interrogates previous words (tokens) in the document, asking them, “are they related to the current word under consideration, and how should this relation be taken into account?”

In this example, we may see lots of words talking about money and investments, which would “push” the embedding for “bank” away from the riverbank and towards the financial institution version. Essentially, attention uses word similarities and math to help bring context to sentences, enabling the LLM to understand meaning and generate natural language responses, with the next obvious word in a sentence predicted using context and data scoring, thus generating a natural-language answer.

Most of the parameters of an LLM are actually in the feedforward layers. Recent research suggests that this is where an LLM stores its facts. The feed-forward layer can be interpreted as asking the model itself if there is any relevant information from the training data that should be included.

For example, if the model saw the words “Aidan Gomez”, one way that the model could understand that Aidan Gomez is a Co-Founder of Cohere is the feedforward layer attaching that additional context to the token.

Large language models: What you need to consider

LLMs are only as good and reliable as the data they’re trained on — and they require massive datasets, which also require technical expertise to build and develop. Then, there are “hallucinations” to think about, whereby outputs are produced that are false or misleading. The danger is that the user may not realize that an answer is hallucinated, as the LLM may respond in a way that sounds authoritative and natural.

However, you can take steps to reduce the possibility of hallucinations. Improving the training data quality and ensuring it is diverse, accurate, and representative of real-world contexts are some examples of how to do this. Additionally, implementing robust fine-tuning methods, incorporating human feedback, and utilizing model validation techniques can help ensure the model generates reliable outputs.

At the end of the day, an LLM is a computer program with all the benefits and limitations that come with technology. It takes significant capital investment, technical expertise, and large-scale infrastructure to maintain. Security can be a challenge, too; the employee productivity and efficiency gains must be balanced against the risk of confidential enterprise data being connected to the LLM. That also means it’s at risk of being manipulated.

Given that an LLM is trained with natural language, it could also ultimately be trained or manipulated to provide specific responses, which could be dangerous, toxic, or unethical. The growing field of responsible AI aims to address these concerns. Choosing the right model for enterprise applications requires strategic alignment, and it may be that you’ll want different approaches for different solutions.

What is the future of large language models?

The future of large language models (LLMs) looks incredibly promising.

Increased capabilities

They have shown enormous versatility with no end of viable LLM use cases. A key development will be equipping them with deeper and broader integrations that enable more direct actions. Instead of merely informing our daily tasks, they can handle many of them for us. Technologies such as RAG will be vitally important to ensure these actions are both reliable and grounded in real-world information. There’s every reason to think they’ll succeed, given time.

Another significant development will be making it easier to train LLMs on niche or personal data. Empowering average users to leverage these models in specialized ways. A doctor, for example, could fine-tune a model for individual patients. Making it much easier for them to keep track of medical history, reoccurring symptoms, and more effective diagnosis.

It's true, however, that there's some reason to be concerned about the processing power required for such advancements. Moore's law (the contention that the number of transistors on a chip doubles roughly every two years) has historically allowed chipmakers to do more with less electricity. However, chip manufacturing is approaching major physical limits, making it tough to accommodate big increases in model sophistication.

Even so, we should be optimistic. Ongoing efficiency improvements and fresh technological innovations can do much to alleviate the power concerns. Audiovisual training

While LLMs are already capable of generating text, they are also being used as part of the infrastructure to produce images. For example, the AI image generator Dall-E uses an LLM to process text prompts to create images.

Audio and video could be the major leap in how LLMs are used, combining them with audio and video elements. They'll be able to create coherent multimedia content. Imagine an AI that can generate a video complete with synced voice overs, background music, and captions—all at the same time. Consider what it could mean for creative marketing campaigns if an LLM could seamlessly bring these elements together.

Given how large language models work, achieving this will require that systems are trained on far more than text. Feeding them rich combinations of formats and media types will teach them how these components fit together. However, to make these systems robust, we'll also need to achieve significant improvements in the underlying processing systems.

It's imperative to find more ways to reduce the inherent randomness that still exists in today's generative AI. It can lead to inconsistent, mismatched, or hallucinatory results. RAG will help here, as will a focus on approximating the sequential creative process humans use.

Workplace transformation

We’ve seen how productivity with LLMs changes as these tools automate routine workplace processes with tasks such as producing performance reports or handling accounts. But there are so many more ways in which they're set to make their mark. For example, automating part of the customer support process by having LLMs handle the initial point of contact for customer queries and providing answers based on domain-specific knowledge.

Shifting businesses to cut out a lot of tedious tasks, leading to streamlined operations and reduced overheads. And because AI systems can monitor real-time performance data around the clock, they can also improve operational efficiency, making adjustments as needed based on changing conditions.

Overall, LLMs taking on the groundwork in workplaces will allow employees to dedicate their time to tasks that require their unique skills and creativity. However, issues surrounding generative AI security need to be addressed. e advocate for its deployment to support and enhance human labor in an ethical way. And working with companies that commit to this is imperative.

Conversational AI

Conversational AI is already impressive, but the future will bring even more advanced capabilities in tone awareness and contextual understanding. This would make an AI assistant that could not only respond to queries but also detect subtle shifts in your tone or emotional state.

With better image analysis capabilities when given access to camera feeds, LLMs could read visual cues to better understand context, making interactions feel much more natural and intuitive.

Moreover, these systems could provide even greater benefits when paired with smart devices such as wearables. For example, by monitoring GPS data and taking customer purchasing patterns into account, an LLM could recommend local products and offers a person might wish to take advantage of. When such integrations arrive, they'll enable AI assistants to assist in daily tasks and personal well-being and safety.

Harness the full potential of large language models

To recap our overview of LLMs, here are the main takeaways:

1. A large language model (LLM) is trained on very large datasets to spot patterns in language, which helps it to process and generate responses to unstructured queries in “human” language.

2. Businesses are using LLMs to develop new products and services, such as 24/7 customer support engines, to gain productivity and efficiency across an enterprise.

3. You’ve likely already come across an LLM being used as a customer service chatbot, a knowledge assistant, or an engine for sentiment analysis or text summarization.

4. LLMs are built using deep learning techniques to create neural networks modeled on the human brain. This creates an adaptive system for models to learn from mistakes and continuously improve.

5. LLMs work via transformer models, which run input through tokenization, embedding, positional encoding, and the attention mechanism before delivering output in natural language.

6. While LLMs learn fast and bring great efficiency and productivity benefits to businesses, they are only as reliable as the data they are trained on, which brings the risk of hallucination, issues with security, and the potential to be manipulated by bad actors. Responsible AI is an evolving field that aims to address these challenges for enterprise use.

7. The future of LLMs is bright with increased capabilities and efficiency making them more accessible. More use cases are arising with audiovisual training, increased productivity in the workplace, and even more conversational AI.

FAQs about large language models

There’s understandably a lot of curiosity about LLMs and many questions going around. We’ve covered some of them already, but here are some more answers to common queries:

How are large language models trained?

Large language models are trained using vast datasets, including text from sources like books, websites, and research papers. Training an LLM with extensive data allows it to find patterns within it, spotting relationships between words, phrases, and sections and incorporating facts present in the training data. After training, models are tasked with generating text that adheres to those patterns. Over time, through feedback and iteration, those generations more closely resemble human-made text. Once models are trained on general bodies of data, you can fine-tune them to use more niche sets of data to make them better at handling tasks for specific topics or industries.

How do I build a large language model?

Building a large language model begins with selecting a suitable architecture, typically one using a transformer model. Next, choose a training dataset that provides the model with the information it needs to meet your requirements. Once the necessary computational resources are set up—either locally or via the cloud—you can begin feeding the model the training data so it can learn the underlying patterns. Throughout the process, you'll periodically test the model's capabilities to monitor its progress. If the model performs well, it’s ready for fine-tuning for your use case. If not, you can gather additional training data to improve its performance.

How do I evaluate large language models?

Evaluating large language models involves running tests and looking at a variety of elements to see how they perform. Are their outputs fully relevant to their prompts? Can they handle unusual or poorly phrased prompts? How frequently do they hallucinate or produce incoherent text? Do they generate content that’s appropriate and in line with regulations? Note that, in general, these checks should not be automated. Collecting feedback from real people who know what to look for is key because it’s the only reliable way to assess real-world application capability.

What is the difference between GPT and LLM?

The difference between GPT (Generative Pretrained Transformer) and LLM (Large Language Model) is that the former is a specific model type while the latter is a broader term for any model designed to process and learn from natural language. All GPTs are LLMs, but not all LLMs are GPTs. LLMs are used for a variety of tasks beyond text generation, such as sentiment analysis or text classification. GPTs, however, focus entirely on generation.

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What are large language models? LLMs explained