What is Large Language Models (LLMs)?

Large Language Models (LLMs) are machine learning models trained to generate and transform text by predicting sequences of tokens. Large Language Models (LLMs) can answer questions, summarize content, and follow instructions based on training data and, in some systems, retrieved sources.

Quick definition

Large Language Models (LLMs) are AI models that generate text based on patterns learned from data.

How Large Language Models (LLMs) works

• Large Language Models (LLMs) generate text by predicting the next token given a prompt.
• Large Language Models (LLMs) can use system prompts and user prompts to shape outputs.
• Large Language Models (LLMs) may use retrieval-augmented generation (RAG) to incorporate external information.
• Large Language Models (LLMs) have constraints such as token limit and sampling settings like temperature (LLM).

Why Large Language Models (LLMs) matters

Large Language Models (LLMs) matters because LLM answers influence discovery, support, and decision-making.

Large Language Models (LLMs) also introduce risks such as hallucinations (AI) when outputs are not grounded.

Example use cases

• Generating an answer to a “what is” query in conversational search.
• Summarizing a document and extracting key steps.
• Producing a response that includes citations when the system uses retrieval.

Related terms

• LLM Answers
• Retrieval-Augmented Generation (RAG)
• Token Limit
• Temperature (LLM)
• Fine-Tuning