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What is an LLM?

AI Writing Terms

A Large Language Model (LLM) is the AI technology that powers tools like ChatGPT, Claude, and specialized writing assistants. LLMs are trained on massive amounts of text to learn patterns in language, enabling them to generate human-like text in response to prompts.

Think of an LLM as a sophisticated autocomplete system. It predicts what words should come next based on patterns it learned from training data, not from actual understanding or knowledge.

How LLMs work

LLMs are trained on billions of words from books, websites, and other text sources. They learn statistical relationships between words, phrases, and concepts - what typically follows what in different contexts.

When you give a prompt, the LLM generates text by repeatedly predicting the most likely next word (or token) based on everything that came before. It doesn't plan ahead or truly understand - it's very good pattern matching.

Popular LLMs

GPT (made by OpenAI) powers ChatGPT and many AI writing tools. Different versions (GPT-3.5, GPT-4, GPT-4o) offer different capabilities and costs.

Claude (made by Anthropic) is known for more nuanced writing and better following complex instructions. It's often preferred for long-form content.

Gemini (made by Google) integrates with Google's ecosystem and offers strong search integration.

Many specialized writing tools use these underlying LLMs with custom prompting and interfaces designed for specific use cases like blog posts.

Limitations

LLMs don't actually understand content. They recognize patterns but can't reason, verify facts, or know when they're wrong. This leads to hallucinations - confident statements about things that aren't true.

They're limited by context windows - only a certain amount of text fits in their "working memory" at once. Long documents may exceed what the LLM can process in one go.

LLMs reflect their training data, including biases, outdated information (they have knowledge cutoff dates), and generic writing patterns that make AI-generated content recognizable.

Using LLMs effectively

Treat LLM output as first drafts requiring editing and fact-checking. Never publish raw LLM output without human review, especially for factual claims or specific details.

Provide detailed prompts and context. LLMs work better when given clear instructions, examples, and relevant information rather than vague requests.

Understand that LLMs are tools, not magic. They can dramatically speed up certain writing tasks but can't replace expertise, original thinking, or strategic content decisions.

Technical concepts

Tokens are how LLMs process text - roughly 3/4 of a word each. Understanding token limits helps you work within constraints.

Temperature controls randomness in output. Lower temperature means more predictable, focused text. Higher temperature adds creativity but risks incoherence.

Context window defines how much text the LLM can "see" at once - your prompt plus its response combined.

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