LLM (Large Language Model)
A large language model is a neural network trained on massive amounts of text that predicts the next token given context.
A large language model (LLM) is a neural network trained on massive corpora of text that learns to predict the next token given the preceding context. Modern LLMs (GPT, Claude, Gemini, Llama) range from a few billion to hundreds of billions of parameters and form the substrate of nearly all 2025-2026 AI products.
In context
LLMs are the foundation under chatbots, AI agents, copilots, code assistants, and most production AI features. They are powerful but bounded: they hallucinate (produce confident wrong answers), they have knowledge cutoffs, and they cannot reliably execute multi-step actions without scaffolding.
Production AI for customer service almost never uses raw LLMs. The standard pattern is RAG (Retrieval-Augmented Generation), the system retrieves relevant documents from a knowledge base and provides them as context, grounding the LLM's response. Combined with guardrails and verification pipelines, RAG-grounded LLMs reduce hallucination rates from the 15-30% baseline for ungrounded models to 0.7-1.5% in the best benchmarks.
The LLM-API cost share of total enterprise AI build cost is typically 8-15%; the dominant cost driver is the surrounding human-intensive work (governance, QA, integration, optimization).
How Auralis uses LLM (Large Language Model)
Auralis builds on multiple LLM providers, with model selection and routing tuned to the workload. The Audit module instruments model behavior in production so model-quality drift surfaces before it reaches customers.