Large Language Models: What They Are, How They Work, and Why Nonprofits Need to Understand Them

When you hear large language models, AI systems trained on massive amounts of text to understand and generate human-like language. Also known as LLMs, they're the engine behind chatbots, automated grant writers, and tools that summarize reports in seconds. They’re not magic—they’re math, data, and computing power working together. But for nonprofits, that math can mean saving hours on paperwork, reaching more donors, or helping staff who aren’t tech-savvy build tools they actually need.

These models don’t think like humans. They predict the next word based on patterns they’ve seen—billions of them. That’s why they can write a fundraising email in seconds, but also why they sometimes make up facts or miss cultural context. That’s where ethical AI deployment, the practice of using AI responsibly, especially in sensitive areas like healthcare, finance, or youth services comes in. You can’t just plug in a model and walk away. You need checks: Who reviewed the output? Is the data private? Are you violating donor trust?

Cost is another big piece. Running big models like GPT-4 or Claude 3 isn’t cheap. But you don’t always need the biggest one. LLM compute budget, the planned spending on processing power, cloud services, and model licensing for AI projects is about smart choices—not just bigger numbers. Many nonprofits get better results with smaller, open-source models that cost a fraction of the price. And with techniques like model compression and thinking tokens, you can stretch your budget further without losing quality.

For nonprofits, this isn’t about keeping up with tech trends. It’s about doing more with less. A case manager can use an LLM to draft client summaries. A development officer can turn a rough draft into a polished grant proposal. A volunteer coordinator can build a simple scheduling tool without writing a single line of code. But all of it depends on knowing what’s possible—and what’s risky.

You’ll find real examples here: how one nonprofit cut its grant-writing time by 70% using a fine-tuned open-source model. How another avoided a data breach by setting clear rules for what data LLMs can touch. And how teams are using sparse Mixture-of-Experts models to get near-top performance without breaking their tech budget. These aren’t theory pieces. These are lessons from the field.

There’s no need to become an AI expert. But you do need to understand the basics: how these models work, where they help, where they mislead, and how to use them without putting your mission or your community at risk. The posts below give you exactly that—no jargon, no fluff, just what you need to start using large language models the right way.