Large Language Models (LLMs)

Large Language Models are neural networks with billions of parameters trained on vast amounts of text. Well-known examples include GPT-4, Claude, and Llama. These models have developed broad language understanding and can write texts, answer questions, produce code, and follow complex instructions. They form the technical foundation for most modern AI applications.

LLMs are based on the transformer architecture and trained in two phases: first they learn general language knowledge from billions of texts (pre-training), then they are fine-tuned for specific tasks (fine-tuning). During use, they process input text and calculate the most likely continuation word by word - considering the entire context.

LLMs enable businesses to automate language-based processes that previously only humans could handle. From automatically answering complex customer inquiries to creating reports to analyzing contracts - LLMs work around the clock, consistently and at scale. For SMBs, this means enterprise-grade AI quality without an enterprise budget.

A tax consulting firm uses an LLM to pre-sort client emails and automatically answer standard questions. The model recognizes whether an inquiry requires a simple appointment booking, a document request, or complex consulting - and acts accordingly.