At this point, it should be unlikely that you haven’t come across some tech term, whether or not you’re into tech. Some are big, strange words; others? Not so much. Some are easy to relate with – you can probably guess what they are, and what they’re used for through their names.
You can readily think of AI (or maybe LLMs) and can’t differentiate between AI and LLMs, prompt, ChatGPT, Gemini, Claude, etc. And, if that’s about all you can narrow AI down to at the moment, that’s fine; The Nebula is here to help you know more.
In this blog, we want to expose you to the term Large Language Models (LLMs). You’ve likely heard or seen it before. You might have heard it used in a conference or conversation and got lost at that point. Or you’re just simply curious about what it means and what it can offer you, especially as a business owner asking, “What is an LLM and why is it so important to my business?”
Guess what? You’re not alone. Large Language Models, as we know them now, have been around for roughly 7 years. So, the haze is understandable. And while there may be much hype both for and against LLMs, it’s also obvious that many people don’t yet fully understand it. So, let’s help you get started on that.
What are LLMs?
LLMs, or Large Language Models, are a branch of AI that is trained on large volumes of data – including books, websites, code, articles, and conversations – to comprehend, generate, and interact with human language.
Think of it like this: A newbie assistant or company secretary has read and studied millions of documents, digested them. Then this assistant is asked a question. You’ll find that they will respond with an intelligible and probably contextually accurate response, and this won’t necessarily be because they’re thinking or intelligent; it’ll simply be because they have learned the rules, forms, or patterns in that language on a very, very large scale in, so much so that they can proffer a response or solution based on what they’ve consumed in that context.
In this case, ChatGPT, Claude, Gemini, and Perplexity are all examples of products built on LLMs. The LLM itself refers to the engine, the framework, of the structure; the product is the interface you’ve been interacting with.
How Do LLMs Actually Work?
LLMs adopt a structure known as a transformer architecture, something you shouldn’t bother yourself with, even while you’re seeking to effectively use them. Of course, you can let us know in the comments if you’d like us to write about that.
On the business level, what you need to know about how LLMs work is that:
- They are “trained” on large datasets and narrow-scoped for specific tasks or niches. Based on this, each LLM has its own customized strengths and weaknesses.
- They provide outputs based on predictions. They operate by predicting the most likely next word or sentence based on each context, in response to your prompt. This is simply why they are fluent and dynamic.
- They don’t know things in the way that you do. Their outputs are based on statistical analysis of the data they are trained with.
It’s the reason why LLMs still appear confident – as you might have experienced – even when their outputs are wrong or inaccurate. This is a factor that businesses should look out for.
Practical Use Cases for LLMs in Businesses
Of course, LLMs are no longer theoretical. They are here already. Small and large businesses globally are already integrating LLMs in five key areas, including:
Customer Support
AI chatbots that are based on LLMs are now used to solve the first-line customer queries 24/7, significantly decreasing response times and allowing human agents to work on more challenging problems.
Code Review and Writing
Developers can use tools such as GitHub Copilot (powered by LLM) to auto-complete code, identify bugs, and write documentation, which helps them to make development cycles faster. The software development market is actually one that is being rapidly redefined by AI.
Internal Knowledge Bases
Internal AI assistants are being developed by companies that search policies, contracts, and documentation and convert company knowledge into instant answers.
Data Analysis
LLMs are being used by business analysts to summarize reports, draw insights out of raw data, and even create SQL queries in plain English.
Practical Limitation: What LLMs Can’t Do (Yet)
Quite unfortunately, LLMs aren’t infinite in what they can do. They still have their flaws. Hence, before you fully invest in and adopt it into your workflows, you must know that:
They can hallucinate
As we mentioned earlier, they can produce false or inaccurate responses with confidence. Therefore, human oversight or supervision must not be neglected.
They have knowledge cutoffs, which account for why some LLMs produce stale information
The majority of the models lack connection or access to real-time data, unless they’re linked to external tools or the web. You can learn more about this here.
They lack true reasoning
LLMs operate by recognizing patterns and language, rather than logical reasoning or creative thinking; therefore, they shouldn’t be completely depended on where critical thinking is required.
They are costly on a large scale
Implementing applications powered by LLM on a large scale includes actual infrastructure and API expenses, which must be included in ROI calculations.
What This Means to Your Business at the Moment – How You Can Proceed With LLMs
Now, in case you are a business owner who’s trying to figure out where and how to begin using AI tools to support your business, you can start by:
- Finding a labor-intensive, time-consuming task in your company. It can be customer emails, internal reports, content creation, etc., and then test an LLM-powered tool for it.
The goal here is to find the most suitable LLM for that task, and your personal preference for the task.
- Don’t build from scratch yet. Test with the available tools like ChatGPT, Claude, Notion AI, etc., before attempting to create your own.
- Involve your tech team. Statistically, the most effective implementations of LLMs occur when business objectives and technical expertise are intertwined and interwoven from the onset of the implementation.
In essence, ensure your tech team builds with your team’s vision in mind.
- Keep a human in the loop. Maintain human oversight over all LLM-assigned tasks, especially ones that affect your customers, legal matters, and your brand image.
In Conclusion
The issue is not whether or not the LLMs will have an impact on your business. They already do. The question is, are you in a better place to gain?
Most of the companies doing very well today are not the most technologically advanced, not really; rather, they are the ones with the most thoughtful and strategic use of AI. They know what a piece of technology does before they determine how they will use it.
Large language models (LLMs) provide a lot of benefits, as we have examined. Shorter execution time, enlarged knowledge base, etc., are all associated with the advent of LLMs in businesses.
However, you can only tap into these benefits when your business demands meet the adequate supply. The difference between the majority of the organizations today that gain value through AI or LLMs and those who do not is largely a function of talent and clarity, not necessarily access to tech tools, a gap worth closing.
And this is where The Nebula comes in. The Nebula serves as a bridge, the nexus, for these relationships. Whether you are a business seeking to add AI to your product or a developer with the skills to work with LLMs collaboratively and practically, The Nebula is the place where that work is done. Reach out to us today to get your dream started.