What are LLMs (Large Language Models)?

A Large Language Model (LLM) is an advanced machine learning model that can understand and produce text in human language. Here’s how it gets its name:

• Large: LLMs rely on enormous datasets containing billions, or, in some cases, trillions, of parameters. During training, the model learns from these parameters, helping it detect complex patterns in language.
• Language: The main function of LLMs is to process and generate natural language, like humans speak and write in. Users interact through prompts, and the LLM responds in a natural way.
• Model: LLMs are AI models: computer programs that learn from vast amounts of historical data to make future predictions, make decisions, and generate coherent language outputs.

LLMs are the engine behind generative AI. They can be used to summarise reports, draft social media copy, and write new code. They’re also useful for businesses, which can use LLMs to give customers more relevant, personalised content. They excel at answering questions, which is ideal in a customer support scenario.

Advancements in artificial intelligence (AI) fueled by LLMs also make it possible for companies to create and deploy AI agents. When prompted by customers or staff, these intelligent systems are capable of solving complex problems using memory, sequential reasoning, and self-reflection.

There’s plenty to learn here, so let’s unpack what makes an LLM unique, how the foundation model works, and how it can benefit your business.

• Generative AI vs. large language models: Is there a difference?
• Use cases for large language models
• How LLMs interpret and generate language
• How are large language models trained?
• What are the advantages of LLMs?
• Should you build your own LLM?
• Three tips for using LLMs like a pro
• Bringing LLMs to life with Agentforce
• FAQs

Generative AI refers to artificial intelligence systems that can generate new content (hence the name). This includes text but also extends to images, code, video or audio files.

Large Language Models are one type of generative AI tool focused specifically on human language understanding, processing, and production. They’re a subset of generative AI, but aren’t the only type of generative model.

To sum up, all LLMs are generative AI; not all generative AI solutions are LLMs. Generative AI can also include models that create images based on prompts, compose original music, and turn text into video clips, for instance.

LLMs and generative AI are gold dust for any task that requires a nuanced understanding of human input.

They can perform tasks as wide-ranging as text generation, content summarisation, and knowledge base creation. LLMs can also produce code and translate languages, as well as classify datasets near-instantly.

Let’s expand on these use cases to show you what’s possible:

• Text generation: LLMs can generate text based on your data, making it more relevant to your customers. For example, marketing teams might use an LLM to draft a call to action for a new product. Commerce teams can also use AI to generate promotions.
• Content summarisation: Users can also ask LLMs to summarise large blocks of text and customise outputs. Summaries can be delivered as shorter paragraphs, bulleted lists, or single sentences.
• Knowledge base answering: Many companies have employee and customer-facing LLM knowledge bases that contain answers to common questions.
• Code generation: It's also possible to use LLMs for code generation once models have been trained in a specific language, such as Python or JavaScript.
• Sentiment analysis: LLMs can collect data from social media posts, emails, texts, and other data sources—and then analyse this data to determine overall user sentiment.
• Language translation: LLMs may also act as translation services, which helps your service team use AI to respond to queries in and translate different languages than their own. The caveat? Context is often absent from translations, so human oversight is recommended.
• Classification and categorisation: Using LLMs, businesses can also rapidly classify and categorise data based on shared elements or characteristics.

Advancements in LLMs have also enabled the use of AI agents, which can complete multi-step tasks, adapt to context in real-time, and provide on-demand support for customers or staff. They can also be customised to meet specific business requirements.

For instance, solutions like Agentforce, the agentic layer of the Salesforce platform, use pre-built skills (as well as low-code custom actions) to power a wide range of out-of-the-box applications, from responding to customer queries to powering product recommendation engines. Agentforce also uses conversational AI, so interactions with agents will feel more natural than robotic.

As mentioned, large language models learn from a vast amount of data to understand, predict and generate human language. To achieve this, they depend on three components: machine (and deep) learning, neural networks, and transformer models. Let’s take a closer look at what each of those components actually means.

Machine learning (ML) algorithms instruct LLMs on how to collect data, discover connections, and identify common features.

Deep learning is a subset of ML that allows LLMs to learn with less human intervention and uses a probabilistic approach to improve accuracy. Consider an LLM that analyses 1,000 sentences. Deep learning tools determine the letters "E", "T", "A", and "O" appear most often. From there, the model extrapolates (correctly) that these are among the most-used letters in English.

Neural networks, also called artificial neural networks (ANNs), are groups of connected nodes that can communicate with each other. These nodes are arranged into layers including input, output, and at least one middle layer—and allow LLMs to process information quickly. These networks are loosely based on the human brain’s neural networks but are far less complex.

Transformer models help LLMs understand language context. Using techniques such as self-attention, these models can analyse sentence structure and word choice to understand how elements of language relate to each other. This allows LLMs to better understand and process user queries.

LLMs understand text differently based on the models they use. Encoder-only models focus on making sense of the text that is provided, while decoder-only models generate text based on a prompt. When you put them together—encoder-decoder—LLMs can understand and generate text, taking on language-driven tasks such as customer service or sales. For example, an LLM-driven AI chatbot might be used to answer customer questions about shipping times, product details, or pricing changes, freeing up human representatives to work on more strategic tasks.

With the right data in place, there are many ways that businesses can use LLMs, such as having your sales team use AI for tasks like generating pitches, all using relevant customer data that speaks to pain points and preferences.

LLM training typically follows a training process like this:

• Pre-training: LLM training always begins with big data, regardless of the types of LLM agents you're using. This data gives the LLM the basis for detecting patterns in human text and making predictions.
• Fine-tuning: Massive datasets can only go so far as they introduce too much variability. To combat this, the AI will then be refined with more specific, higher-quality datasets that align with specific use cases.
• Reinforcement learning: Lastly, there’s the human element. The LLM is now trained using feedback from real people. This back-and-forth helps the LLM avoid misleading outputs and improve model performance.

While this is a basic overview, there are actually various different training models to consider, each of which has its perks depending on the LLMs use case and complexity. Let’s talk through them now:

Zero-shot learning sees LLMs trained on the fly. Users ask questions and LLMs sort through connected data sources to find answers. Initial accuracy is typically low but improves over time.

In a few-shot approach, data scientists provide a small selection of relevant examples to help LLMs establish baseline connections. Few-shot training significantly improves accuracy in targeted areas.

Chain of thought (CoT) training walks LLMs through a simple reasoning process. Instead of asking a single question, CoT breaks it down into multiple parts. Here's an example:

Standard prompt:

Steve has 20 shirts. Half of his shirts are short-sleeved and half of those shirts are blue. How many blue shirts has he got?

CoT prompt:

Steve has 20 shirts.

Half of his shirts are short-sleeved. This means he has 10 short-sleeved shirts.

Half of these shirts are blue, which means he has 5 blue shirts.

While the prompt itself isn't particularly complicated, CoT provides a step-by-step approach to problem-solving that shows an LLM how to answer the question. This approach can then be applied to other questions.

Fine-tuning and domain-specific models provide additional contextual information for targeted use cases. For example, a company looking to improve its analysis of social media sentiment might provide its LLM with detailed information about how to understand specific words and phrases within the larger context of social platforms.

In this type of model, rather than looking at the text itself, the model translates it into numbers — called vectors. By using numbers, computers can use machine learning to more easily analyse how words and sentences are placed together, making sense of the context and semantic meaning to identify relationships between the words.

In a multimodal model, LLMs are trained to use multiple data formats for input and output. Along with text, these formats may include audio, video or image data.

LLMs offer a host of advantages for organisations. These include reducing or eliminating manual processes and the ability to discover new trends and insights using available data sources.

Here's a look at some of the top benefits of LLMs:

• Adaptability: Since LLMs use the transformer architecture model, instead of explicit rules that are "hard coded," it can adapt to dynamic and unpredictable prompts it may receive.
• Flexibility: There's no single way to use LLMs. Businesses can customise these tools to act as AI assistants, copilots, or autonomous agents.
• Performance: As LLMs analyse and incorporate more data, their performance improves, reducing the time between query and answer.
• Efficiency: Because much of LLM learning happens independently of human oversight, staff are free to take on other strategic tasks.

All of this leads to smarter, faster decision-making and enhanced productivity, sharpening the edge businesses have over their competitors.

The simple answer? Probably not.

The more complete answer? In most cases, building your own LLM is expensive, time-consuming and unnecessary.

It's expensive because you need to invest in the expertise and infrastructure to develop a bespoke language model. It's time-consuming because you need to provide a wealth of training data and ensure the training results in accurate outcomes. And it's unnecessary because, in most cases, you're reinventing the wheel.

Using pre-trained, open-source LLMs that come with built-in security guardrails often provides the best balance of performance and protection. Businesses can leverage the power of models trained using trillions of data points, without worrying that issues in code may lead to inadvertent compromise. You can supplement the LLM model’s information by using a RAG (retrieval-augmented generation), which combines your company’s most relevant and proprietary data.

Let’s round things off with three actionable tips to help you get the most out of your LLMs:

• Craft precise prompts: Accurate LLMs thrive on accurate instructions. The more contextually relevant information you can offer, the better the output. Provide additional information such as tone, format, and background details, as well as examples to tailor the LLM to the nuances of your request.
• Experiment with different models: There are dozens of ways to use large language models. It’s important to choose a model that aligns with your needs. Some are better for reasoning and complex problem-solving; others are for low-latency, real-time conversation. Experiment with different tools to see which one is the right fit, and don’t be afraid to fine-tune your own if you have a specific requirement.
• Leverage AI agents: AI agents can provide scalable, on-demand support for customers or staff and can be customised to meet specific business requirements. This frees up your team for more strategic work. As a plus, many AI agent solutions can be built with little to no coding experience.

Large language models are inching ever closer to a complete, contextual understanding of communication. While oversight remains a critical component in LLM use, these models offer a way to bridge the gap between human insight and IT operations by allowing us to speak the same language.

Ultimately, this will offer businesses better flexibility, greater efficiency, and more opportunities to deliver an exceptional service to customers.

Now that you have a deeper understanding of AI, as well as LLMs, you can take a tour of Agentforce. With Agentforce you can build autonomous AI agents using the LLM of your choice, helping your company get more done — offering a boost in ROI and productivity.