Large Language Model (LLM)

A Large Language Model (LLM) is a neural network with billions of parameters, trained on massive text datasets, that can understand and generate human-like language. LLMs are the core technology powering modern AI chatbots, assistants, and agents.

LLMs work by predicting the most likely next token (word or word piece) given all the preceding tokens. Through training on trillions of tokens of text data, they learn grammar, facts, reasoning patterns, coding abilities, and conversational skills.

Major LLMs include: GPT-4/GPT-4o (OpenAI — powers ChatGPT), Claude 3.5 Sonnet/Claude 3 Opus (Anthropic — known for safety and long context), Gemini 1.5 Pro (Google — multimodal with massive context), Llama 3 (Meta — leading open-source model), and Mistral Large (Mistral AI — efficient European model).

Key LLM concepts: parameters (the learned values that define the model — more parameters generally means more capable), context window (how much text the model can process at once), tokens (the units of text the model works with), temperature (controls randomness vs. determinism in outputs), and prompts (the inputs that guide model behavior).

For AI agent builders, choosing the right LLM involves balancing capability, speed, cost, and context window size. Platforms like Chipp make this easy by offering multiple model options and allowing builders to switch between them based on their agent's needs.