Many people think of Large Language Models (LLMs) primarily in terms of chat. After all, ChatGPT brought LLMs into the mainstream and kicked off the AI revolution. However, the potential of LLMs extends far beyond simple conversation. Business leaders who understand how to properly implement LLMs can unlock significant value across their organizations. This guide will help you understand the fundamentals of LLMs and provide practical strategies for their implementation, backed by real-world success stories.

Simon Willison describes LLMs as “calculators for words”, and it’s an apt comparison. Just as calculators revolutionized numerical computations, LLMs are transforming how we process and generate text. These models can understand context, generate human-like text, and perform complex language tasks with remarkable accuracy.

To implement LLMs effectively, you need to understand a few key concepts. The way you “ask” an LLM to perform a task—called prompting—significantly impacts its output. LLMs also have limits on how much text they can process at once (their “context window”). Understanding these basics helps you identify where LLMs can add the most value to your business.

But the most important question is: does your problem beg for an LLM solution? Just as your business should beg for an app—where the solution is so clearly needed that building an app is the obvious choice—your problem should beg for an LLM. The best LLM implementations feel inevitable rather than forced.

What makes a problem beg for an LLM? Look for repetitive text-based tasks that humans can do but are tedious and time-consuming. The volume should be high enough that manual processing would be impractical. The task should require understanding and processing text but shouldn’t demand deep specialized expertise. And you should be able to clearly define and measure success.

On the flip side, be wary of using LLMs for tasks requiring deep domain expertise, processes needing perfect accuracy, creative work requiring original thinking, or tasks with high stakes or legal implications. LLMs are powerful tools, but they’re not suited for everything.

Let’s look at three real-world examples where the problems clearly begged for LLM solutions:

At Podcraftr, we use LLMs to automatically convert newsletters and blog posts into engaging podcast scripts. This was a perfect fit because the task is repetitive but requires understanding context, the volume would be impossible to handle manually, the output can be reviewed and adjusted if needed, and the transformation follows consistent patterns.

Objective Zero uses LLMs to create training scenarios for support providers. They needed a way to generate realistic but safe training scenarios, provide immediate feedback to trainees, and scale their training capabilities without adding more human trainers. LLMs were the obvious choice for creating these low-stakes practice environments.

Many organizations are implementing AI-powered search for their documentation and help centers. This feels inevitable because traditional search (which mainly looks for exact matches of words) can fail if users don’t word their query in the same way as the documentation. When someone searches “how do I cancel my subscription?” they should find the right help article even if it’s titled “Managing Your Account.” AI-powered search can understand the meaning behind the question, not just the specific words used. As companies’ documentation grows larger and more complex, having an AI that can understand what users are actually asking for becomes essential.

If you’re considering implementing LLMs in your organization, start small. Choose a well-defined, low-risk project for your first implementation. Ensure your solution integrates smoothly with existing workflows, and plan for human oversight where necessary. Track both technical metrics (accuracy, response time) and business metrics (ROI, user satisfaction).

Most importantly, remember that LLMs are tools for transformation, not magic solutions. The best implementations arise from clear needs where an LLM solution feels inevitable. When evaluating potential projects, ask yourself: “Does this problem beg for an LLM solution?” If you find yourself forcing the fit, it might be better to explore alternative approaches. But when you find those tasks that clearly call for LLM capabilities, you’ve likely discovered an opportunity for significant business value.

Ready to explore how LLMs could transform your business operations? Contact Twin Sun for a consultation on implementing AI solutions that drive real business value.