AI is disrupting the tech industry in a myriad of ways, with the introduction of tools like ChatGPT, Midjourney, and DALL·E seeing millions of users every day. So, it’s no surprise that the generative AI market is expected to see rapid growth, rising from 11.3 billion USD in 2023 to 51.8 billion by 2028.
And with Gartner reporting that 70% of organizations are currently exploring generative AI, it’s clear that more and more companies are exploring ways of incorporating AI tools, like large language models (LLMs), into their tech toolbox. But what could that look like for your business?
In this article, we’ll explore how organizations can leverage LLMs for their business and overcome the challenges that come with implementing this new technology.
What is a large language model?
LLMs are machine learning models that have been trained on large datasets to synthesize information and predict the words or phrases that come next. For some businesses, using ChatGPT or similar platforms for one-off tasks may be enough, but there are ways to make LLMs work even better for you.
For example, say you provide an LLM with your cafe’s ingredient list. It might generate the sentence “I like my latte with…” with results such as “low-fat milk,” “whole milk,” or “croissant.” This capability could benefit your company with ordering ingredients, designing new menu offerings, or providing more personalized omnichannel customer experiences.
There are a variety of ways to provide custom company-specific knowledge to the model for your unique use cases. For example, you can fine-tune the model on your smaller dataset or use retrieval-augmented generation (RAG) to add relevant pieces of context to the query. There’s also the option of using LLM agents and tools, which empower models with a certain degree of autonomy, giving them ways to connect and access entire databases (like Google Drives, SQL, or CSV files).
Any of these processes will work to elevate a base language model into one that is more applicable to your specific business needs — and is much more affordable and efficient compared to training from scratch, which is incredibly expensive and time-consuming.
Using LLMs for your business needs and goals
It’s important to understand that while implementing LLMs can offer many benefits, they may not be suitable for every business or endeavor — and that is something our team at The Lab helps clients understand.
However, this process can be particularly useful if your organization has large databases that teams struggle to access or understand. Whether by using fine-tuning, RAG, or LLM Agents, you can enable:
The democratization of data: By providing LLMs with targeted, high-quality datasets, sales, marketing, and business development teams can find information quickly and easily. This allows business intelligence and data teams to direct their attention away from day-to-day requests and toward valuable, innovation-driven projects. We helped a major company supplement the LLM with relevant data to create a chat interface that could quickly pull up answers for their teams.
A reduction in time, money, and human error: Let the model do the hard work of digesting hefty documents. With tools like Intelligent Document Processing (IDP), the model analyzes and extracts data from documents, enabling teams to query questions to the model, cutting down on time spent searching or waiting for the answer and reducing human error.
Innovative experiences for end-users: Whether you’re building an internal or customer-facing tool, LLMs can help you optimize user experiences. It can expedite menial tasks, enhance customer support, personalize content, and enable companies to move faster. We’re currently working with an international brand to leverage their LLM-powered tool and pull valuable insights from customer chat conversations to enrich customer data profiles and provide more accurate and personalized responses.
Overcoming the challenges of implementing LLMs
With new tools come new challenges — and some familiar ones, too. Here are a few complexities associated with implementing an LLM, and how to solve them.
Getting buy-in and alignment
Exploring new tools often means working in uncharted territory, making it difficult to prove ROI and achieve cross-team alignment.
Collaborating closely with a client’s data and development teams, The Lab explores the pros and cons of implementing LLMs for an organization's unique needs and builds a business case that outlines the best path forward.
To get the best answers, you have to ask the right questions. LLM user interfaces are often designed as simple text boxes that provide a scary blank canvas for those unsure of what to ask.
Prompt engineering is the process of structuring content that a generative AI model can understand, and there are a few ways to achieve this. Development teams can build prompt engineering directly into the back-end by injecting a few words before a user’s request to help the model better answer the question. You can also create documentation around the prompting process, or provide examples of prompts within the interface itself.
Worried about disclosing your data with an AI? You’re not alone — in a study done by Predibase, 40% of companies were worried about sharing proprietary data and information with LLM vendors.
The Lab helps our clients mitigate those concerns by developing architectures that limit access to files, databases, and other resources within a specific network. This helps ensure your data stays yours and out of public access. We also outline the privacy policies of LLM vendors to provide transparency for clients.
Despite the challenges and intricacies of LLMs, the benefits and impact they can deliver are worth investigating. The Lab is here to help — our cross-functional teams can identify opportunities based on your unique business needs and assess whether LLMs are worth your investment.
Co-Written by Jaime Chang