Large Language Model (LLM)

Definition

A large language model (LLM) is a type of artificial intelligence model trained on massive text datasets to understand and generate human language. LLMs use transformer architecture with billions of parameters to perform tasks including text generation, summarization, translation, code writing, reasoning, and question answering.

Prominent LLMs include OpenAI's GPT-4, Anthropic's Claude, Google's Gemini, and Meta's Llama. These models are accessed through APIs or deployed on-premises, and they form the foundation of most generative AI applications.

Why It Matters

LLMs represent the most significant shift in enterprise technology since cloud computing. They enable applications that were previously impossible or impractical: natural language interfaces to databases, automated document processing, intelligent search across unstructured data, code generation, and conversational AI assistants.

For enterprises, LLMs create opportunities across every function: customer service automation, knowledge management, content creation, code review, legal document analysis, and operational decision support. The challenge is not whether LLMs are useful -- it is deploying them in production with reliability, security, cost management, and governance.

Key Concepts

Tokens -- The units LLMs process (roughly 4 characters per token in English). Input and output tokens drive cost.
Context window -- The maximum amount of text an LLM can process in a single request (ranging from 8K to 1M+ tokens)
Fine-tuning -- Training an LLM on domain-specific data to improve accuracy for particular tasks
RAG (Retrieval-Augmented Generation) -- Supplementing LLM knowledge with real-time data retrieval from external sources
Prompt engineering -- Designing inputs that produce optimal model outputs

How BAG Uses It

Our AI platform integrates multiple LLMs for different tasks: frontier models (Claude, GPT-4) for complex reasoning and analysis, smaller models for routine classification and extraction. We use RAG pipelines backed by vector databases for enterprise knowledge retrieval, and MCP servers for structured tool access. We help clients select, deploy, and optimize LLMs for their specific use cases with a focus on production reliability and cost management.

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