What is a Large Language Model (LLM)?
An LLM is a transformer-based model trained to predict tokens, enabling tasks like summarization, question answering, and reasoning over text. In enterprises, LLMs is the core foundation of retrieval-augmented generation (RAG), agents, and analytics narratives, provided that governance and cost controls are in place.
How does a Large Language Model (LLM) work?
LLMs consume tokenized text and produce tokens step by step. Quality depends on model size, training data, fine-tuning, and the context you supply (prompts, retrieval). Tool use and structured outputs make LLMs actionable in production.
When should you use it? (Typical use cases)
Knowledge assistance with RAG over your documents/data.
Automated drafting: emails, reports, knowledge base articles.
Entity extraction and classification at scale.
Multilingual support for customer and internal content.
Benefits and risks
Benefits
- Broad task coverage
- Fast time to value via APIs
- Improves with context and feedback
Common pitfalls/risks
- Hallucinations without grounding
- Compliance risks
- Token/cost management
Antire and Large Language Model (LLM)
Antire helps select models (open/proprietary), design prompts and context, and implement guardrails. We focus on measurable outcomes: answer quality, latency, and unit economics.
Services
Tailored AI & ML
Related words: Generative AI, Tokenization, Context window, Embeddings, Fine-tuning