Part of the Analytify BI Glossary, clear definitions of the business intelligence, analytics, and modern data stack terms that matter.
A large language model (LLM) is a neural network with billions of parameters trained on massive text corpora to understand and generate human language, enabling capabilities such as conversation, summarisation, code generation, translation, and reasoning over context.
LLMs are the foundation technology of the generative AI era. Every AI feature shipped in 2024-2026 — from ChatGPT to Microsoft Copilot to Google Gemini to GenBI tools — runs on an LLM under the hood.
For analytics teams specifically, LLMs unlock natural-language interaction with data. Users no longer need to write SQL or build dashboards; they ask questions in English. Combined with RAG and a semantic layer, LLMs become safe, governed analytics co-pilots.
An LLM works through several layered concepts:
Modern LLMs in 2026 have hundreds of billions to trillions of parameters and context windows of 128k-1M tokens, enabling them to read entire books or code repositories in a single prompt.
A SaaS analytics chatbot uses an LLM (e.g. Claude, GPT-4, Gemini) under the hood. When a customer asks “Show me revenue by product last quarter,” the system: (1) retrieves relevant warehouse rows via the semantic layer; (2) constructs a prompt with the data + the question; (3) sends to the LLM; (4) the LLM generates a chart specification + a written summary; (5) the BI tool renders the chart and shows the summary. The LLM never invents data — it formats and explains what was retrieved.
2026 LLM landscape:
Industry-leading general-purpose LLMs. Strong reasoning, code, and tool use.
Strong on safety, long-context reasoning, and code. Popular for production AI agents.
Google’s flagship LLM family. Tight integration with Google Cloud and Google Workspace.
Leading open-weight LLMs. Self-hostable for compliance and cost control.
Enterprise-focused alternatives with niche strengths.
Code (Codestral, DeepSeek Coder), reasoning (o1, o3), and embeddings (text-embedding-3) models for specific workloads.
LLM stands for “Large Language Model.” It refers to a class of neural networks with billions of parameters trained on massive text data to understand and generate language.
OpenAI GPT-4/5, Anthropic Claude (Sonnet, Opus), Google Gemini, Meta Llama, and Mistral are the most-used LLMs in production. Choice depends on capability needs, pricing, and self-hosting requirements.
On their own, no — LLMs hallucinate numbers and table names. Combined with RAG and a semantic layer that constrains what data they can access, LLMs become safe and useful for analytics.
An LLM is the underlying language model. An AI agent is a system that uses an LLM plus tools, memory, and planning to autonomously accomplish multi-step tasks.
Yes for open-weight models (Llama, Mistral, DeepSeek). Self-hosting requires GPUs (H100, A100) or specialised inference hardware and adds operational complexity. Most teams use managed APIs unless compliance or cost demand self-hosting.
Fine-tuning modifies the LLM’s weights with new training data — expensive and inflexible. Prompting (including RAG) injects context at query time without changing the model — cheaper, faster to update, and produces verifiable outputs.