What is a Large Language Model (LLM)? - Definition & Meaning
A large language model (LLM) is an AI model trained on massive amounts of text that can understand and generate human-like language. Learn how LLMs work.
Definition
A Large Language Model (LLM) is an AI model based on deep neural networks trained on billions of words of text. LLMs can understand, generate, translate, and summarize human-like text. Well-known examples include GPT-4, Claude, and Gemini.
Technical Explanation
LLMs are based on the transformer architecture, introduced in the paper "Attention is All You Need" (2017). They consist of millions to trillions of parameters optimized during the training process on large text corpora. Training proceeds in phases: pre-training on unlabeled data (next-token prediction), followed by fine-tuning and alignment via RLHF (Reinforcement Learning from Human Feedback). Inference requires substantial compute and is optimized via techniques like quantization, caching, and batching. Context windows range from thousands to hundreds of thousands of tokens.
How Refront Uses This
Refront integrates LLMs as the engine behind the AI agents. The model analyzes ticket descriptions, generates code solutions through the Cursor MCP integration, writes project summaries, and drafts quotes. By combining retrieval-augmented generation (RAG) with LLMs, Refront delivers context-aware responses based on project-specific data.
Examples
- •An LLM analyzes a ticket description and automatically generates a structured plan with technical tasks.
- •The Refront AI agent uses an LLM to generate code that fixes a specific bug based on the error message.
- •An LLM summarizes an extensive client conversation into three key points that are created as tickets.
Related Terms
Frequently Asked Questions
What is the difference between GPT and an LLM?
GPT (Generative Pre-trained Transformer) is a specific LLM developed by OpenAI. "LLM" is the umbrella term for all large language models, including GPT, Claude, Gemini, Llama, and others. GPT is therefore an example of an LLM.
How does an LLM know the correct answer?
An LLM doesn't literally "know" what is correct. It predicts the most likely next word based on patterns learned from training data. Quality depends on the training, the instructions (prompts), and any fine-tuning on specific tasks.
Can LLMs hallucinate?
Yes, LLMs can generate plausible-sounding but factually incorrect information, known as hallucination. Techniques like RAG and grounding help mitigate this by connecting the model to verified data sources.
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