What is an LLM, basics.

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Understanding LLMs for Beginners

When you hear terms like LLM, SLM, or just Model, it can sound a bit complicated, but let’s break it down.

• Model: This refers to a machine learning system designed to perform a specific task, in this case, understanding and generating human language.

• LLM (Large Language Model): A model that is “large” because it has been trained on massive amounts of text data (think millions or billions of sentences) and contains billions of parameters (the “knobs” it adjusts to improve predictions).

• SLM (Small Language Model): A smaller, less complex version of an LLM, designed for tasks that don’t require as much power or storage.

How They Work

At their core, these models function by predicting the most probable next word in a sentence based on the context of the words that came before it. This is called language modeling, and it’s how they generate coherent, human-like responses.

For example:

If you start with the phrase “The sky is”, the model might predict the next word as “blue”, because that’s the most likely word based on the training data it has seen.

Key Vocabulary

• Training:

This is the process of teaching the model by showing it vast amounts of text data. The model adjusts its parameters to improve its ability to predict the next word or understand relationships between words.

Think of it like learning a new language: the more examples you study, the better you get.

Key facts about training:

• It requires massive computational power (think supercomputers or thousands of GPUs working together).

• It is extremely expensive and time-intensive, sometimes taking weeks or months to complete.

• Inference:

Once the model is trained, it’s ready to be used. Inference refers to the process of applying the trained model to make predictions or generate responses.

For example, when you type a question into ChatGPT, the model is performing inference to give you an answer.

Key facts about inference:

• It is less computationally demanding than training, but still requires good hardware for larger models.

• Most of the cost for businesses using LLMs comes from inference, as it happens every time someone uses the model.

What Makes an LLM “Large”?

The “large” in LLM refers to both:

1. Data Size: The amount of text it has been trained on. For example, GPT-3 (a famous LLM) was trained on hundreds of gigabytes of text from books, websites, and more.

2. Parameter Count: Parameters are like the “brains” of the model. More parameters mean the model can handle more complex tasks, but it also requires more memory and power to operate.

• A small model might have a few million parameters.

• A large model like GPT-3 has over 175 billion parameters.

Why Does Size Matter?

• Larger Models: Tend to be more accurate and capable of understanding nuanced or complex prompts. However, they’re also slower and more expensive to use.

• Smaller Models: Faster and cheaper, but might struggle with difficult or context-heavy tasks. These are great for lightweight applications like chatbots for customer support.

Real-World Applications of LLMs

1. Chatbots: Like customer support bots or personal assistants (think Siri or Alexa).

2. Translation: Converting text from one language to another.

3. Content Generation: Writing articles, code, or even stories.

4. Summarization: Reducing long articles or documents into shorter, concise summaries.

5. Medical or Legal Analysis: Helping professionals analyze complex documents or data.

Limitations of LLMs

It’s important to understand what LLMs can’t do well:

• They don’t truly “understand” like humans do; they only predict based on patterns in the data they’ve seen.

• They can sometimes make errors, like generating factually incorrect or nonsensical answers (called hallucinations).

• They require careful oversight in critical tasks like medicine or law to avoid mistakes.

This is a basic introduction to help you understand LLMs. The next time you hear about AI and models like ChatGPT, you’ll have a better grasp of how they work and what they’re capable of.

1. Examples of Use Cases: Use ChatGPT, DeepSeek, Claude or any LLM or model that can help you compose emails, notes, make your email sound nicer if you’re in a bad mood, or help you communicate sterness without too many F bombs, it’s great.

2. Simplify Concepts/Learning: If you you’re here, you’re most likely trying to further your own education. When learning a new subject it can be hard. Use the models to help you understand. The only thing to consider is that sometimes they will hallucinate, aka make things up. With that being said, double check by using perplexity.ai to ensure that it’s verified by multiple sources