What Can Large Language Models (LLMs) Be Used For?
A friendly introduction to leveraging large language models (LLMs) in enterprise applications
Large language models (LLMs) like GPT-4 are impressive in their ability to perform a wide range of tasks seemingly out of the blue. If you're an executive managing language- and text-based use cases, you may be wondering how to leverage this technology in a way that delivers tangible benefits to your organization.
In this blog post, we break down the barriers to implementing LLMs in enterprises by explaining in non-technical language what they are, the tasks they can already perform effortlessly, and the best techniques for ensuring their secure behavior. And we'll address the question on many people's minds: Will AI replace my job?
What are large language models?
A large language model is the product of a complex algorithm that has learned from massive amounts of data. Specifically, LLMs use the ingenious "Transformer" architecture for neural networks that researchers at Google pioneered about six years ago. Transformers introduced a new way for computers to understand tricky linguistic phenomena like synonyms, complex sentence structures, and the meaning of pronouns.
Thanks to this new technique, today's language models can process and understand text data almost as well as a human brain, but much faster. In generative AI systems, LLMs are capable of generating coherent, human-like output in response to user instructions.
This makes LLMs suitable candidates for many text-based tasks across industries, such as:
- Copywriting and editing
- Customer support
- Automated text summarization
- Information extraction
How are large language models created?
LLMs represent an impressive technological advance, and creating these large Transformer models is no easy feat. Companies that specialize in LLMs pour millions of dollars into their training, using massive computing resources and vast amounts of data. Training consists of complex pre-training and fine-tuning steps – check out the intro to LLMs on our developer’s blog for an in-depth explanation.
Given all these requirements, it's a blessing that once trained, LLMs can be shared and reused endlessly. You're probably familiar with OpenAI and its famous family of GPT models. But there are many, many more models out there. Some, like GPT, are proprietary and can only be accessed through an application programming interface (API), others are open source and can be used with an inference service like AWS SageMaker.
Can large language models replace humans?
So if LLMs are so good at generating fluent, appropriate copy for all kinds of use cases, does that mean that people in these industries are going to lose their jobs en masse? The answer is no – if you care about your content and its use, it's wise to adopt a human-in-the-loop approach rather than replacing everyone with an LLM and letting your business run its course.
The inner workings of an LLM are notoriously difficult to understand, which is why they're often referred to as black boxes. For this reason, it's important to continually evaluate and test the results of LLMs to ensure that they continue to solve the problems they were designed to address.
Programmers, copywriters, and data analysts are all subject matter experts in their respective fields and are the best candidates to judge the quality of an LLM's output. So, instead of fearing the impact of large language models, these groups can start using them – and see how this change speeds up their work process immensely.
This is similar to how graphic designers already use generative AI with tools like Adobe Firefly in their work process. Does this mean they will be replaced by AI? No, but their output is higher and, thanks to the sheer limitless capabilities of these technologies, potentially more innovative.
Strategies for harnessing the power of large language models
No new technology is without risk, especially one as disruptive as large language models. The most worrisome property of LLMs that has emerged is their propensity for hallucination, where the model invents facts. Other concerns relate to the training data of LLMs, such as the fact that low-quality data sourced from the internet can contain implicit or explicit biases, which the models then inherit. Does this mean you shouldn't use LLMs in production? Of course not.
The LLM space is evolving rapidly, and researchers and practitioners alike are constantly uncovering new strategies for making these models work in a production environment in a way that's both valuable and safe. As a company working on real-world use cases, we have identified the following techniques as the most powerful solutions to address the shortcomings of LLMs.
Retrieval augmentation generation (RAG)
One of the most promising approaches to applying LLMs to real-world use cases comes from retrieval augmented generation (RAG) pipelines. RAG systems combine an LLM with a classic document search module to get the best of both worlds: human-like fluency and fact-checked data. RAG is versatile and infinitely customizable, but at its core is this idea: by feeding your LLM relevant, up-to-date information, you can ensure that its answers are correct and significantly reduce the risk of hallucinations.
Prompts are human-generated instructions to an LLM. They allow us to use natural language to get the LLM to do our bidding - a bit like persuading another person to do something, except of course you're dealing with a machine.
Prompt engineering is the discipline of finding the most efficient prompt methods to guide the LLM. Some of them are even funny, like telling the LLM to "take a deep breath" and "think one step at a time.” Others are more practical – like few-shot prompting, where you include examples of the task in your prompt.
LLMs can perform many tasks right out of the box. But if you want an LLM to excel at a very specific task – like writing copy in a certain tone, or handling documents from a niche field like medicine – then a basic model may not be performant enough for you. Fortunately, fine-tuning is now available for LLMs, too: by subjecting a model to additional training steps on a hand-picked, curated dataset from your organization, it can become the domain expert you need it to be.
Fine-tuning is a well-established technique in machine learning, and it can be used to improve your retrieval models, too. In fact, any Transformer-based model that you use in your LLM pipeline can be fine-tuned to match the specific use case your application requires.
Large language models in production: a snapshot
Let's look at a few scenarios in which LLMs can increase the speed and quality of a team’s output.
LLMs can redesign and summarize existing content to speak to different demographics and pain points. For example, an LLM-powered application can rewrite complex administrative or technical text into simpler language to make it accessible to a wider audience.
But, of course, that's not the limit of what LLMs can do – they can also write product descriptions, blog posts, and other content entirely from scratch. With RAG, you can give them the context they need to write informative and helpful text. With prompting, you can guide them toward the desired outcome. And fine-tuning lets you train the language model to match your writer's voice.
ChatGPT's ability to converse in natural language as a human would is arguably what sets it apart from previous language models. By integrating the capabilities of an LLM like GPT-3.5 or GPT-4 into a RAG pipeline powered by their own data, organizations can deliver high-quality customer support around the clock with little to no human input.
Moreover, companies can enable users to find information faster and more accurately by placing an LLM on top of technical documentation, internal reports, and other knowledge bases.
Programming and data science
Programmers were among the first to understand the impact that LLMs could have on their work, thanks to the timely introduction of GitHub's Copilot. The "AI pair programmer," which uses OpenAI's Codex LLM, allows developers to use natural language prompts to complete code snippets or even write entire programs on the fly.
Generating valid and efficient SQL code is another area where LLMs can significantly speed up developers' work. As of now, a data analyst must still manually review generated queries, but more robust solutions are on the horizon.
Building efficiently with large language models
If you want to start building industry applications with LLMs, consider using a centralized platform where your team can come together and build a solution in a fraction of the time it takes to build it from scratch.
deepset Cloud, our LLM platform for AI teams, lets software teams expedite their building process through the use of composable LLM-powered pipelines that can connect to different providers and let you stay model-agnostic. Discover powerful components to instrument the most common language-based tasks, agents, prompt nodes, and more — and quickly integrate LLM functionality into an existing or new application.
Watch our head of product Mathis' free webinar on building real apps with LLMs to see strategic ways to make LLMs interact with your existing data, reduce hallucinations, and validate the quality of the user experience.