LLMs Basics
A foundational overview of how LLMs work - covering key concepts like tokens, context windows, and the difference between standard and reasoning models.
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A foundational overview of how LLMs work - covering key concepts like tokens, context windows, and the difference between standard and reasoning models.
Large Language Models (LLMs) are AI systems trained on large amounts of text to understand and generate natural language. They can help with a wide range of tasks like answering questions, summarizing documents, translating text, etc. LLMs can be utilized in everything from chatbots and search to business automation and research.
LLM (Large Language Model)
LLMs are trained to understand and generate human-like text based on patterns in data. They're great at tasks like writing, translating, summarizing, and answering straightforward questions. However, they may fall short when tasks require deeper reasoning, step-by-step logic, or complex decision-making.
R-LLM (Reasoning-Enabled Language Model)
R-LLMs take things a step further. They’re designed not just to generate text, but to reason through problems. These models can handle more complex tasks like explaining decisions, solving multi-step problems, or making logical inferences by breaking down their thought process and offering more structured, explainable answers.
When you send a message, the LLM breaks your text into small units called tokens (roughly one word or part of a word), processes them to understand context and meaning, then generates a response - token by token. The total amount of text the model can process at once (your prompt, conversation history, and its response combined) is called the context window - the larger it is, the more information the model can work with at once.
Tokens are the unit LLMs use to read and generate text. Users need to understand this because:
It directly affects Compute Block (CB) consumption
It explains why longer prompts or responses cost more
It sets up the concept of context windows
Example: "The quick brown fox" = ~4 tokens
A context window is the total amount of text an LLM can hold in its "working memory" at once - including your instructions, the conversation history, any documents you've shared, and the model's response. Once the conversation exceeds this limit, the model starts to "forget" earlier parts of the exchange.
Dive into the Overview of LLMs to find out which LLMs you can use in Blockbrain
Figure out which one works for you best by reading How to Choose the Right LLM
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