TLDR:
A Large Language Model (LLM) is a deep neural network trained on massive text corpora to understand and generate human-like language; modern LLMs (GPT-5, Claude Opus, Gemini, Llama) underpin the generative AI products driving most enterprise AI adoption in 2025-2026.
How LLMs Work
LLMs are built on transformer architectures with billions to trillions of parameters. Training proceeds in stages: pre-training on large text datasets to learn statistical patterns of language, supervised fine-tuning on curated instruction-following data, and reinforcement learning from human feedback (RLHF) to align outputs with human preferences. The result is a model that can answer questions, write code, summarize documents, and increasingly reason through multi-step problems.
Business Use Cases
LLMs power customer support automation, content generation, code assistants (Copilot, Cursor, Claude Code), legal contract review, data extraction, and increasingly autonomous AI agents. For startups, LLMs are commonly accessed through APIs from foundation model providers (OpenAI, Anthropic, Google, Mistral) rather than trained from scratch, dramatically reducing infrastructure investment.
Legal and Regulatory Considerations
LLM deployment raises significant legal questions: training data copyright (multiple lawsuits including NYT v. OpenAI), output liability (who is responsible for harmful or false generations), data privacy (GDPR and KVKK constraints on training and inference data), and high-risk classification under the EU AI Act (effective in tiers through 2026-2028). Startups deploying LLM-powered products should map regulatory obligations early and document training data provenance.