If you’ve started learning about Generative AI or working with large language models, you might have noticed that the terms can seem more complicated than the ideas themselves. Words like tokens, embeddings, temperature, and context window are used a lot, but they’re not always explained clearly. In this article, I’ll break down the key LLM terms you need to know in simple language.
Key LLM Terms
Here’s a simple breakdown of the key LLM terms, explained as they’re used in real systems.
1. Tokens: How LLMs Actually Read Text
Many beginners think LLMs read text word by word, but that’s not the case. Instead, they process tokens, which are smaller pieces of text.
Tokens are like small parts of a sentence that the model can understand. Sometimes a token is a whole word, sometimes just part of a word, or even just punctuation.
For example, the sentence:
"ChatGPT is powerful"
might be broken into tokens like:
["Chat", "G", "PT", " is", " powerful"]
This is more important than it might seem. Every LLM has limits based on tokens, not words. When you hear phrases like:
- “This model supports 8K context”
- “You exceeded the token limit”
it’s really about how much text, measured in tokens, the model can handle at one time.
2. Context Window: The Model’s Working Memory
The context window is how much information the model can remember at one time.
An easy way to picture this is to imagine explaining a problem to a coworker. If the conversation goes on too long, they might forget what you said earlier. LLMs work in a similar way.
If your input exceeds the context window:
- Older parts get truncated, or
- The model simply ignores them.
That’s why long conversations or big documents can sometimes cause the model to give inconsistent answers.
3. Embeddings: Turning Text into Numbers
LLMs don’t really understand text; they work with numbers. Embeddings are the way text is turned into numbers the model can use.
Embeddings put text into a space with many dimensions, where words with similar meanings end up close to each other.
For example:
- “dog” and “puppy”: close together
- “dog” and “car”: far apart
This is what makes systems like semantic search, recommendation engines, and Retrieval-Augmented Generation (RAG) work.
In real use, embeddings help your AI system find the right information, not just create text. Many beginners focus on prompts, but in real systems, embeddings are just as important.
4. Temperature: Controlling Creativity
Temperature sets how predictable or creative the model’s answers will be. Here’s how it works:
- Low temperature = safe, predictable answers
- High temperature = more creative, sometimes chaotic answers
With a low temperature (like 0.2), the model chooses the most likely next word. This is helpful for:
- Code generation
- Fact-based answers
- Structured outputs
With a higher temperature (like 0.8), the model tries more options, which can be good for:
- Writing
- Brainstorming
- Creative tasks
In real-world use, setting the temperature is more about managing risk than changing style. More creativity also means a higher chance of mistakes or inconsistent answers.
5. Hallucination: When the Model Sounds Confident but Is Wrong
Hallucination is one of the most important terms to know. It happens when an LLM gives information that sounds right but is actually wrong. For example:
- Making up facts
- Inventing references
- Giving incorrect explanations confidently
This isn’t a mistake; it’s just how LLMs work. They guess the most likely next word, not always the most accurate one.
In real systems, hallucination is a big problem. That’s why methods like RAG and using tools help connect the model to real data.
6. RAG: Giving the Model Memory
RAG is a key idea in today’s AI systems. Instead of just using what the model learned during training, RAG lets it:
- Retrieve relevant information from a database.
- Use that information to generate an answer.
It’s like taking an open-book test instead of a closed-book one.
This is how tools like AI chatbots for company documents, knowledge assistants, and internal search systems work.
Without RAG, LLMs are limited to their training data. With RAG, they can work with your data.
Closing Thoughts
Once you know these key LLM terms, it’s clear that building AI systems isn’t just about using an LLM. It’s really about working within certain limits.
This is where many beginners struggle. They focus on the model, but real-world systems are more about how you use it.
If you found this article helpful, you can follow me on Instagram for daily AI tips and practical resources. You may also be interested in my latest book, Hands-On GenAI, LLMs & AI Agents, a step-by-step guide to prepare you for careers in today’s AI industry.
