What are LLMs? A simple guide for customer-first brands

It feels like you can’t go a day without hearing about AI. From news headlines to your social media feed, everyone is talking about Large Language Models (LLMs), generative AI, and the future of work. For leaders at small to medium-sized businesses (SMBs) and B2B companies, it can all feel like a lot of noise. You’re busy running your business, not building complex AI systems.

But here’s why you should care about LLMs. They are fundamentally changing how we talk to customers. Understanding this technology, and how it works, is the first step to using its power.

Think of this guide as your translator. At Gladly, we turn complex ideas into simple, clear insights. We'll show you what LLMs are, how they work, and why they are key to delivering better service while being more efficient.

What is an LLM?

For many years, AI in customer service meant simple, rule-based chatbots. You’d ask a question, and if it didn’t match a pre-programmed keyword, the bot would fail. This kind of "static automation" was clunky and frustrating for both customers and businesses.

LLMs are different. They represent a new era of CX, moving beyond fragmented ticketing systems and bolt-on AI.

At its core, a Large Language Model is a sophisticated type of AI that understands and generates human-like text. The "large" in LLM refers to two things: the massive size of the model itself and the enormous amount of data it was trained on. These models learn from vast datasets—think of all the books, articles, and websites on the internet. This training allows them to understand grammar, context, and a huge range of topics.

With this knowledge, LLMs can answer questions, write documents, help with emails, and even have conversations. They don’t actually understand meaning the way humans do, but they get very good at figuring out what words are likely to come next, which helps them give useful replies.

Unlike simple bots that follow rigid scripts, LLMs are conversational. They can respond to questions, write emails, summarize long articles, and even create marketing copy. In a business context, this means they can handle a wide variety of tasks that used to require a human.

But not all LLMs are created equal. The real power comes from a specific type of LLM known as "agentic AI." It can perform meaningful actions to resolve issues. For example, agentic AI can process a return, update an order, or apply loyalty points, all within secure, defined parameters. This is the difference between a bot that gives you information and an AI that solves your problem.

Here’s how LLMs work:

• They learn by reading lots of text. This helps them figure out how words and sentences usually fit together.

• When you have a conversation with an LLM, you can ask questions or give it instructions using regular sentences.

• The LLM tries to give helpful and clear answers based on everything it’s learned.

• LLMs don’t “think” like people do, but they can be very useful for explaining things, telling stories, or helping with tasks at work.

In simple terms, LLMs use patterns they’ve learned from tons of text to help people with all kinds of writing and language tasks. They make interacting with technology feel more natural and conversational.

How to use LLMs for your business

Businesses of all sizes can use large language models in practical ways to improve how they operate, communicate, and serve their customers. Here are just some of those ways.

• Personalized outreach: LLMs can write personalized sales and marketing messages, making it easier to reach out to clients with emails or LinkedIn messages that feel custom. This can help sales teams get better responses and close deals faster.

• Customer support: LLMs can power smart chatbots that answer client questions, help with support tickets, and explain technical issues in real time, day or night. These systems handle complex, multi-step conversations using industry-specific language, leading to faster answers and improved customer satisfaction.

• Document automation: LLMs can quickly process, summarize, or draft documents, contracts, proposals, and reports. This saves time on repetitive paperwork and helps teams focus on more valuable work.

• Data analysis and reporting: LLMs can review large datasets, find trends, and turn complex information into clear reports. This helps decision-makers spot opportunities, manage risks, and act based on up-to-date insights.

• Knowledge management: Custom LLMs trained on company data can answer employee questions about internal processes, products, or client history. This makes it easier for teams to find information and onboard new team members.

• Compliance and risk: LLMs can assist in reviewing legal documents, spotting compliance issues, and analyzing regulations, especially important in industries where accuracy is critical.

• Content creation: B2B companies use LLMs to generate articles, whitepapers, marketing materials, and more, all while keeping the brand’s voice consistent and optimizing for search engines.

LLMs help companies work smarter, talk to clients more effectively, automate routine tasks, and make better business decisions, giving them an edge in a highly competitive market.

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How to use LLMs for SMB

The key to using LLMs effectively is about integrating the tech into your existing processes to create a seamless experience for both your customers and your team. Here are a few ways that LLMs are transforming small businesses.

1. Self-service that actually works

When a customer is on your website looking for a tracking number, a legacy bot might make them navigate pre-set buttons, like "Where is my order?" If their question is phrased differently, the bot might fail. An LLM, by contrast, understands natural-language requests and can provide the tracking information instantly. It can even proactively offer relevant shipment details without being prompted. This helps to reduce customer effort and improve satisfaction.

2. Empowering your team with AI

Your support agents are your most valuable asset. LLMs can act as a powerful companion, providing instant summaries of long conversations, drafting responses in your brand’s voice, and even translating languages on the fly. This frees up your human team to focus on the empathetic conversations that build long-term relationships and drive revenue. AI assistance reduces onboarding time, boosts productivity, and lowers average conversation time.

3. Personalization at scale

A customer who feels known is a customer who stays loyal and buys more. Good LLMs are built on customer-centric platforms that can access conversation histories, order details, and preferences to provide personalized service.

This means a customer never has to repeat themselves, and every interaction feels like a conversation with a trusted friend. This continuous, single conversation thread is a key differentiator. It's a foundational capability that enables AI to deliver a deeply personal experience.

Building your CX strategy on the right LLM

You wouldn’t build a house on a shaky foundation, and the same is true for AI. For an LLM to be truly effective, it needs to be built on the right data. Many legacy platforms are built on a "ticketing" system, which treats every customer issue as a new, separate case. This creates fragmented, incomplete data that makes it impossible for an AI to understand the full context of a customer relationship.

It’s only through a single, lifelong conversation that an LLM can access a complete history of every interaction a customer has ever had with your brand, across every channel. This rich, contextual data is what we call Customer AI. It's the intelligence layer that powers efficient, personal conversations at scale, without compromising on experience. Our philosophy is simple: your AI is only as smart as its foundation.

This foundational difference is crucial. With Gladly, customer relationship intelligence is embedded everywhere and continuously learns, listens, and proactively engages. This approach allows for a symbiotic relationship between AI and humans, where technology amplifies human connection.

Avoiding common pitfalls

While LLMs are incredibly powerful, they are not a magic bullet. It's important to understand their limitations and how to avoid common pitfalls.

Hallucinations and brittle logic

Sometimes, LLMs can generate plausible-sounding but factually incorrect information. This is a significant risk when an LLM is trained on a wide-open dataset without a strong, business-specific foundation. The risk is that the AI will provide incorrect information that can frustrate customers or harm your brand.

Loss of brand identity

A generic AI can make your brand sound robotic and impersonal, which can damage customer loyalty. Your brand isn’t just what you say, it's how you sound and how you treat people.

Lack of control

One major challenge with complex AI is the lack of transparency. With many platforms, it’s difficult to know what the AI is doing or why it's making certain decisions. AI only works if you trust it and when it’s trustworthy.

Next steps for your business

The era of AI is here, and it's an opportunity, not something to fear. By moving from being AI-curious to AI-confident, you can choose a platform built for this new world.

With a platform like Gladly, you no longer have to compromise. You can deliver the kind of radically personal service that builds loyalty and drives revenue, all while improving efficiency. If you’re curious about a new kind of conversation for your SMB or B2B brand, the right Large Language Model is waiting for you to try here.

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