Large Language Models: How They Work and How To Use Them (2024) – Shopify UK

What if you could write marketing copy, design your ecommerce website, code all the pages, balance your books, and answer customer service inquiries—all at the same time? This is the promise of large language models. Businesses are increasingly using enterprise-grade LLMs to handle a wide array of business tasks, from copywriting to coding to customer care. These enterprise applications can operate at massive scale with security features you might not find in free, general-purpose LLMs like ChatGPT. Here’s an overview of large language models from an ecommerce perspective.

What are large language models?

Large language models (LLMs) are artificial intelligence models that use deep learning to understand, generate, and manipulate human language—and some are even multi-modal, meaning they can generate text, imagery, video, and audio. LLMs are trained on massive datasets that include text from books, websites, articles, blogs, and more. LLMs are able to ingest these huge datasets through unsupervised learning—meaning they can be trained using unlabeled data. Once trained, a large language model can be fine-tuned with labeled data and supervision, with data scientists giving it feedback on its output or adjusting its parameters.

LLMs can perform myriad language-related tasks, including text generation, language translation, summarization, and sentiment analysis. While these generative AI models lack the reasoning capacity of the human brain, they can generate text that convincingly mimics human language by using a complex, probabilistic algorithm to infer what letters or words should come next.

Some of the most widely used LLMs include GPT and o1 from OpenAI, Google’s Gemini, Anthropic’s Claude, and Meta’s Llama, to name a few. These LLMs power popular chatbots and generative AI tools.

How large language models work

• Data
• Architecture
• Training
• Maintenance

LLMs rely on deep learning, a subset of machine learning that uses multiple layers of neural networks—computer programs that learn from data in a way that’s inspired by the human brain. Neural networks are made up of layers of interconnected nodes that work together to process information and make predictions.

The key ingredients for training and using LLMs are data (what you train the model on), model architecture (the type of model you’re training), training (how you train the model), and maintenance (how you keep the model running).

Here’s a closer look:

Data

Large language models are pre-trained on massive amounts of text data culled from books, articles, and code, among other things. The LLM training process involves feeding the model large, text-based data sets, and allowing it to learn patterns and relationships within that training data (more on that in a moment). As a general rule, more data—and higher quality data—leads to more robust, capable AI models.

The transformer architecture can be trained from unstructured data (essentially, written information that isn’t labeled or broken out in a spreadsheet). This is sometimes called unsupervised learning.

Architecture

LLMs are transformer models—which means they’re a powerful type of neural network that’s especially effective at handling language, whether it’s writing, translating, or answering questions about a text. You can think of a transformer as a particularly attentive reader. When it reads a sentence, it doesn’t just look at each word one by one. Instead, it pays attention to all the words in the sentence at once, figuring out how they relate to each other contextually.

For example, in the sentence “The cat sat on the mat,” a transformer can understand that “the cat” is the subject and “the mat” is the object—even though the words are separated by several other words.

Training

A large language model’s performance—its ability to understand and generate human language—is based on patterns its neural networks learn during training. Here’s a simple analogy for how this works: Imagine you’re trying to teach a dog to fetch a ball. You throw the ball, and the dog runs after it. If the dog brings the ball back, you give it a treat. If the dog doesn’t bring the ball back, you don’t give it a treat.

• In this analogy, the dog’s brain is like a neural network, and the treat is like a reward.
• The dog’s brain is made up of neurons that are connected to each other. Similarly, the neural network is made up of nodes that are also connected to each other.
• When you throw the ball, you’re giving the dog input data. The dog’s brain processes this data and decides what to do. When you ask an LLM a question or provide a text prompt, its neural network also processes input data and makes predictions based on that data.
• If the dog brings the ball back, it gets a reward, which strengthens the connections between the neurons in its brain that led to that decision. Similarly, when a neural network makes a correct prediction, the connections between the nodes that led to that prediction are strengthened.

What kind of predictions is the LLM making? Essentially, it’s predicting the next likeliest word in any given sequence of words based on prior context. This is known as token probability: the likelihood that a particular token (a word or sub-word) will be the next one in the sequence. LLMs generate text one token at a time, predicting the next token based on the preceding tokens and the model’s training data.

Training often involves hundreds of billions of tokens and substantial computational power. Distributed software systems across multiple servers handle these large-scale models. If this sounds complicated, it definitely is! Training large language models requires immense technical expertise.

Maintenance

Vendors must maintain large language models to ensure optimal performance. LLMs are not “live,” so to speak—they don’t have access to all digitized written content as it’s published online. Instead, they’re dependent on the recency of the data on which they’re trained. Thus, in order to stay current, they need to be trained on new data periodically.

LLMs can be fine-tuned to provide useful answers based on less input. Nonetheless, training LLMs still requires human feedback for quality control—even if the process is technically “unsupervised.” One way to do this is through prompt engineering, where data scientists refine input prompts to guide LLMs to perform specific tasks or generate desired responses.

Benefits of large language models

An ever-increasing number of businesses use large language models to generate text, write code, and handle customer service inquiries, among other things. This helps explain why so much of the $184 billion global AI economy is focused on LLMs. The many benefits of LLMs include:

• Versatility. LLMs can perform a wide range of tasks, such as text generation, text classification, translating languages, sentiment analysis, and question answering, all within a single model.
• Scalability. LLMs can handle vast amounts of unstructured data, allowing them to process and analyze large datasets efficiently. This is valuable to those working in ecommerce, since a big part of sales success comes from understanding and gleaning insights from the data you collect from customers and website visitors.
• Ever-improving accuracy. Due to their large-scale and advanced training techniques like self-attention and in-context learning, LLMs generate increasingly accurate and context-aware responses.
• Automation. LLMs reduce the need for manual effort in generating content, automating tasks such as chatbot interactions, report writing, and even code generation. This saves your team time and resources, letting you focus on other tasks that may require more strategic thinking.

Limitations of large language models

Large language models are actively revolutionizing business as we know it, but the technology still has notable limitations: 

• Dependency on large datasets. LLMs require vast amounts of sequential data and an enormous model size to achieve high performance. This makes them notoriously resource-intensive to train and maintain. There are also legal challenges surrounding what can be used as training data, and if compensation is required. 
• Privacy. LLMs aren’t immune to data breaches, and any data fed to an LLM is at risk of being leaked in the case of a breach. Using LLMs to process proprietary data and customer information can represent a security risk.
• Struggles with niche requests. LLMs may struggle to provide precise answers for niche queries, requiring techniques like retrieval augmented generation—essentially, retrieving data from outside sources (like search engines) and using that information to create a more accurate and detailed answer.
• Context limitations. While LLMs can process large amounts of input text, they may lose track of context in longer conversations or documents, leading to less relevant outputs. This issue especially manifests in AI-powered search engines or when humans ask LLMs long, complex questions.
• Hallucinations. LLMs can make mistakes. In fact, ChatGPT even includes this disclaimer under its prompt bar: “ChatGPT can make mistakes. Consider checking important information.” Mistakes often stem from incorrect information that was fed to the model, but LLMs can also invent false information—this is called a “hallucination.”
• Bias. LLMs can reproduce the biases in their training data, favoring particular demographic segments or cultures.

Uses of large language models for ecommerce

• Chatbots and virtual assistants
• Content generation
• Personalized shopping experiences
• Search optimization
• Data analysis
• Automating administrative tasks
• Translation
• Fraud detection

LLMs can optimize or automate an array of specific tasks. For the most part, using an enterprise-grade LLM is similar to using an everyday LLM tool like ChatGPT and Google Gemini. The main difference is that paid enterprise programs have collaborative tools and integrations with other software, and you’ll typically sign an agreement with the LLM provider to ensure you have the security features necessary to keep your intellectual property safe.

You can use plug-ins or write code to connect your data to the LLM interface, and large companies with complex operations may commission a proprietary LLM made for their specific needs. Here are some of the many ways that LLMs have woven their way into ecommerce:

Chatbots and virtual assistants

LLMs power sophisticated AI chatbots that can handle customer inquiries 24/7. These chatbots answer questions from customers, providing instant responses to frequently asked questions. They can also guide customers through purchasing processes, improving customer satisfaction and reducing the load on human support teams.

Content generation

LLMs can generate content such as product descriptions, marketing copy, and blog posts. Provide the LLM with a prompt outlining the type of content you’re looking for and any parameters, then tailor the output to your liking. Depending on your specific needs, you can use a general use LLM like ChatGPT or a more specialized tool for ecommerce business owners, like Shopify Magic.

Personalized shopping experiences

By analyzing user behavior and preferences, LLMs can generate personalized product recommendations. This can increase user engagement and boost conversion rates. Why? Because customers are more likely to purchase items that align with their interests, and the LLM helps a business understand what those interests really are.

Search optimization

Does your ecommerce store have a built-in search function? LLMs can improve search functionality by interpreting user queries more accurately. This helps customers find relevant products, reducing frustration and improving the likelihood of conversions.

Data analysis

LLMs can analyze reviews, customer feedback, and social media interactions to extract sentiment and insights about your target audience. Understanding customer opinions helps you refine your offerings, address customer pain points, and identify market trends.

Automating administrative tasks

One of the most useful things a large language model can do is take administrative work off your plate. To this end, LLMs can assist in managing inventory levels by predicting demand based on historical sales data. They can help automate pricing strategies by analyzing competitor pricing and market trends. They can also handle your day-to-day bookkeeping, letting your finance team focus on more complex strategic objectives.

Translation

LLMs can understand and generate text in multiple languages and instantly translate from one language to another. By doing so, they enable you to engage with global customers without the obstacle of persistent language barriers. 

Fraud detection

AI powers a lot of corporate fraud detection endeavors, and LLMs are especially good at detecting fraudulent communications like phishing emails. An LLM can intercept and flag these communications before anyone on your team considers engaging with them.