Artificial Intelligence (AI) is all around us from voice assistants and recommendation algorithms to self-driving cars and advanced chatbots. But when terms like “Machine Learning” and “LLMs” get thrown into the mix, it’s easy to feel lost. Are they all the same? How do they relate?

In this article, we’ll break down the difference between AI, Machine Learning (ML), and Large Language Models (LLMs) in plain English. Whether you’re a curious learner, a business leader, or just want to understand the buzzwords, this guide is for you.

What Is Artificial Intelligence (AI)?

Artificial Intelligence is a broad field in computer science focused on creating machines that can perform tasks typically requiring human intelligence. These tasks include reasoning, problem-solving, recognizing patterns, understanding language, and learning from experience.

Everyday Examples of AI

• Voice assistants like Siri or Alexa
  
• Fraud detection systems used by banks
  
• Smart thermostats that learn your schedule
  

AI is the umbrella term that includes many subfields—including Machine Learning.

What Is Machine Learning (ML)?

Machine Learning is a subset of AI that focuses on enabling computers to learn from data without being explicitly programmed. Instead of writing a specific set of rules, we give the system data and let it “learn” patterns to make predictions or decisions.

Types of Machine Learning

1. Supervised Learning – The model learns from labeled data (e.g., spam vs. not spam).
   
2. Unsupervised Learning – The model finds patterns in unlabeled data (e.g., customer segmentation).
   
3. Reinforcement Learning – The model learns through trial and error (e.g., game-playing AI like AlphaGo).
   

Think of ML as the engine that powers many of today’s smart applications.

What Are Large Language Models (LLMs)?

Large Language Models are a type of Machine Learning model trained on massive amounts of text data. They’re designed to understand, generate, and manipulate human language in a highly sophisticated way.

LLMs in Action

• Chatbots like ChatGPT
  
• Writing assistants (grammar suggestions, content generation)
  
• Language translation tools
  

LLMs use a type of ML architecture called a “Transformer,” which allows them to understand context and relationships between words—making them capable of producing human-like responses.

Why This Matters

Understanding these distinctions helps you:

• Ask better questions when evaluating tech solutions
  
• Communicate more clearly with technical teams
  
• Avoid being misled by vague or incorrect AI marketing
  

As AI continues to shape the future of work, business, and everyday life, knowing the basics gives you an edge.

What This Looks Like for SaaS Finance Teams

If you’re in SaaS finance (CFO, controller, or RevOps) you’ve probably seen AI show up already. Not in a dramatic way. Just things running faster, forecasts looking sharper, and reports almost writing themselves.

Here’s how it’s actually helping:

• Faster close
  AI tools can catch anomalies and speed up reconciliations, so close doesn’t drag into next week.
  
• Smarter forecasts
  ML can spot patterns in churn, upgrades, and expansion to improve accuracy.
  
• Easier reporting
  LLMs help turn numbers into clear takeaways for board decks or performance summaries.
  
• Clearer communication
  You can explain complex finance topics across teams without sounding like a spreadsheet.
  

You don’t need to be technical to take advantage of this. Just knowing what’s possible puts you ahead.

Final Thoughts

AI is the big picture. Machine Learning is a way to make that picture come to life. And Large Language Models are one powerful brushstroke in the ML toolkit specializing in human language.

Still have questions or want to see how LLMs like ChatGPT are changing business operations and SaaS workflows? Reach out or explore more on the TrueRev blog.