technical-term · Models & inference · foundational · Reviewed

Large language model

A learned model that produces or scores token sequences from context; in an agent, it is the reasoning component rather than the complete system.

Definition

A large language model estimates language-conditioned outputs from a context of tokens. It may generate text, structured data, code, or tool-call arguments, but it does not by itself provide identity, permission, durable memory, execution, verification, evidence, or accountability.

The model class is established and rapidly evolving; capabilities and limitations differ materially by model, configuration, and task.

Why it matters

Model behavior is probabilistic and context-dependent.

Enterprise claims must be measured on the complete deployed system and workload, not inferred from a public leaderboard.

System anatomy

Parameters
Learned weights encoding statistical patterns.
Context
The finite token sequence supplied at inference time.
Decoder
The process that selects output tokens under configured sampling.

Important distinctions

AI agent
A model generates candidate decisions; an agent loops, uses tools, maintains state, and operates inside a governed system.
Knowledge base
Model weights are not a current, attributable store of enterprise truth.

Implementation signals

  • Select models through workload-specific evaluations
  • Record model identity, version, configuration, and routing decisions

Failure modes

  • Treating fluency as factual authority
  • Assuming one benchmark ranking predicts production fitness

Sources and further study

  1. Artificial Analysis — Artificial Analysis Intelligence Benchmarking Methodology

    A transparent methodology for comparing model quality, price, latency, throughput, and cost per task. Its composite indices are useful evidence, not a substitute for use-case-specific evaluation.

    Use in this library: Independent benchmark. Composite benchmark results change over time and do not establish enterprise fit, safety, controllability, or acceptance for a specific workload. No leaderboard values are reproduced here.

    research · research · Published 2026-06
  2. NIST — Towards best practices for automated benchmark evaluations

    Guidance on the integrity and limitations of automated benchmark evaluation practices.

    Use in this library: Official guidance. Guidance describes recommended practice; citation does not prove that a control is implemented or effective in a particular environment.

    guidance · guidance · Published 2026-01-30
  3. OpenAI — A practical guide to building agents

    Defines a practical agent around a model, tools, instructions, and a run loop that continues until an exit condition is reached.

    Use in this library: Primary definition. The guide is first-party product guidance and a simplified starting architecture, not a complete regulated-enterprise control model.

    guidance · guidance · Published 2025