LLM (Large Language Model)
AI models trained on vast amounts of text that can understand and generate human language. GPT-4, Claude, and Llama are leading examples.
In-Depth Explanation
Large Language Models (LLMs) are AI systems trained on massive text datasets that can understand, generate, and reason about language. They represent the current state of the art in natural language AI and power most modern AI applications.
What makes a model "large":
- Billions of parameters (GPT-4: ~1.7 trillion estimated)
- Training on trillions of tokens of text
- Enormous compute budgets for training
- Emergent capabilities that appear at scale
Key capabilities of LLMs:
- Understanding: Comprehend complex queries and context
- Generation: Produce coherent, contextual text
- Reasoning: Solve problems and make logical connections
- Translation: Convert between languages
- Summarisation: Condense long content
- Coding: Write, explain, and debug code
- Analysis: Extract insights from text
The LLM landscape includes:
- Closed-source: GPT-4 (OpenAI), Claude (Anthropic), Gemini (Google)
- Open-source: Llama (Meta), Mistral, Falcon, MPT
LLMs have transformed from research curiosities to essential business tools in just a few years.
Business Context
LLMs are the foundation of modern AI applications, enabling everything from chatbots to document analysis to code generation.
How Clever Ops Uses This
We help Australian businesses navigate the LLM landscape, selecting the right models for their use cases and building robust applications on top of them.
Example Use Case
"Using an LLM to analyse contracts, generate reports, power customer service, or automate routine communication tasks."