AI & Machine Learning Glossary: Key Terms for Modern Businesses

AI is the hottest topic in tech right now, and there’s a lot of machine learning and AI terminology being thrown about — including some terms that could confuse even the most seasoned tech pros.

So what do AI terms mean?

If you’re partnering with an AI development agency, managing an internal tech team, or looking to use AI in your business, understanding AI and machine learning, along with the jargon used to describe them, is crucial.

Whether you’re a startup, an SMB, or an enterprise, getting up to speed with AI and its associated lingo will give you a competitive edge in a rapidly evolving space.

That’s why we’ve put together this glossary — a list of AI definitions for businesses. You can scroll down the A to Z list to quickly reference new terminology and get an overview of what each term means.

Read on for artificial intelligence terms explained in a way anyone can understand.

A-Z Artificial Intelligence Glossary Index

Generative AI

Generative AI definition: Generative AI is a type of artificial intelligence that can create new content, such as text, images, music, or code, by learning from existing data.

Instead of just analyzing information, it produces something original — like a chatbot that drafts emails, a design tool that suggests layouts, or an app that generates marketing copy.

For startups and enterprises, generative AI offers faster content creation, streamlined workflows, and innovative customer experiences.

Read more → How to Integrate AI Into an App

Large language model (LLM)

LLM definition: Large language models (a type of neural networks) are a type of generative AI system. They’re trained on huge amounts of text-based data, and they use their knowledge to forecast word sequences.

LLMs learn patterns in language — like grammar, meaning, and context — to perform tasks that include writing, summarizing, translating, answering questions, and analyzing text.

Today, the most popular AI tools and assistants are powered by large language models. This is why tools like ChatGPT, Claude, Gemini, and Copilot can understand instructions, hold conversations, and generate natural-sounding responses.

For businesses, LLMs can speed up tasks like drafting emails and reports. They can power chatbots to respond naturally to customers. They can also analyze text to find trends and meaning.

Read more → AI Agents vs AI Systems for Software Architecture

Prompt engineering

Prompt engineering definition: Prompts are the instructions you give an AI model to elicit your desired response. Prompt engineering (also known as prompt design) is the act of crafting those prompts.`

Well-designed prompts can make an AI give more accurate answers and perform more complex tasks. Understanding prompt engineering best practices can help you get the most from generative AI tools.

Some best practices include breaking complex tasks into smaller steps, giving relevant background information, and using examples to guide AI output.

Transformer model

Transformer model explained: A transformer model is a type of neural network architecture that can handle sequential data more efficiently and effectively than older models. It’s the foundation for most modern large language models.

Transformer models process all words at once. This helps them weigh the importance of different words in relation to each other, understanding context better. It also makes them faster to train because we can use parallel computation.

Basically, in a Transformer, each word looks at every other word in the sentence and learns which ones are most important to interpret its meaning.

Overfitting/underfitting

Overfitting vs. underfitting definition: Overfitting is when an AI model learns training data too well. It’s like memorizing test answers without truly understanding the subject matter.

Underfitting is when a model is too simple to capture patterns in data. It often happens when there’s insufficient data or the wrong kind of data for the task you’re asking the AI to complete.  

The key difference between overfitting and underfitting is that, where overfitting performs poorly only on new data, underfitting performs poorly on both training and new data.

Read more → How to Use AI Classification to Build More Efficient Apps

Natural language processing (NLP)

Natural language processing definition: Natural language processing (NLP) is a field of artificial intelligence. It focuses on getting AI to understand, interpret, and generate human language in a meaningful way.

Key tasks in NLP include tokenization, text generation, translation, and summarization. You can also use NLP to understand a piece of text, detecting its topic, key facts, or sentiment.

Fine-tuning

Fine-tuning definition: Fine-tuning means taking an existing AI model and training it for specific tasks or use cases. For example, you can take a model that already understands patterns from data like language rules and grammar. You can then apply it to a specific task, like legal documents, by training it with additional data.  

Understanding how to fine-tune AI models helps you adapt AI to your needs without training an AI from scratch. It also helps improve its task-specific performance.

Bias/fairness in AI

Bias in AI definition: Bias in AI refers to errors or unfairness produced by skewed training data. This bias can cause models to favor specific outcomes or groups over others. 

Some ethical AI bias examples include:

• Facial recognition: People of certain ethnicities are misidentified because they are underrepresented in training datasets
• AI hiring systems: Historical data shows a preference for male candidates, so AI systems continue to prioritize male applicants

Understanding the issues to look out for helps you understand how to mitigate bias in machine learning. You can use diverse and representative training data and test models for biased behavior.

Read more → AI for Software Development: Best Practices and Tools

Hallucination (in LLMs)

Hallucination in LLMs definition: A hallucination in an LLM is the production of output that seems plausible but is factually incorrect.

Hallucinatory output in LLM occurs because the model is optimized to generate text that sounds plausible based on patterns in its training data, not to check its answers against verified facts. An LLM can create references that don’t exist, reel off incorrect statistics, and even generate fictional people.

It does so confidently — which is why fact-checking generative AI output is essential. When you understand why LLMs hallucinate, you get better at verifying output and at fine-tuning your AI model.

RAG (retrieval-augmented generation)

Retrieval-augmented generation definition: Retrieval-augmented generation (RAG) is a technique that combines a large language model (LLM) with an external knowledge source (like a database or search engine).

This technique helps generate more accurate, fact-based responses, allows the AI to pull from up-to-date sources, and reduces the number of hallucinations your AI system creates. It takes less time and money than fine-tuning an AI system.

Use cases of RAG in production include enriching the model’s context with internal company documents to support better decision-making, or with up-to-date financial reports to provide accurate investment insights.  

In summary, RAG is a smart retrieval layer that enriches an LLM’s context at runtime, allowing it to generate more accurate and grounded responses.

Add AI to your business with Cheesecake Labs

As you’ve probably gathered from reading the AI terms and definitions in our glossary, there are lots of ways to use artificial intelligence for business.

Here at Cheesecake Labs, we help our clients:

• Build AI-powered web and mobile apps
• Deploy conversational chatbots
• Build AI systems for automation and efficiency
• Use AI for strategy and execution

As part of our AI development services, we offer end-to-end support — we don’t just take care of engineering, but also AI strategy, design, integration, and deployment.

We also have wide-ranging AI model and infrastructure expertise, and we’re not wedded to any single system. An agnostic approach helps us find the right solution for our clients, every time.  

So what next?

When you understand AI, you get to know all the amazing things it can do for your business. And when you work with AI specialists, like the team at Cheesecake Labs, you start turning some of those ideas into reality.

Curious how these concepts apply to your product? Explore our AI Development Services or contact us for more info.

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