Large Language Models (LLMs) are the technology behind ChatGPT, Claude, and almost every AI tool making headlines. Understanding what they are (and aren't) is the foundation for every enterprise AI conversation.
The Definition
A Large Language Model (LLM) is a type of artificial intelligence trained on vast amounts of text to understand and generate human language. "Large" refers to the model's size - billions of parameters (learned patterns) trained on datasets spanning a significant portion of the internet and published text.
In practical terms: an LLM is a system that can read, understand context, generate text, answer questions, summarise documents, write code, and reason through multi-step problems, all based on patterns learned from its training data.
How LLMs Actually Work (Without the Maths)
LLMs learn by reading enormous amounts of text and learning the patterns of language: which words follow which, how ideas connect, what constitutes a reasonable response to a question. They don't "understand" in the human sense. They're exceptionally good pattern-matching systems that produce outputs that look like understanding.
Think of it this way: an LLM has read millions of insurance claims, thousands of legal contracts, and hundreds of medical research papers. When you ask it to analyse a claim, it draws on patterns from every similar document it's processed. Not by remembering specific documents, but by recognising the patterns that make up competent claims analysis.
Key implication: LLMs are incredibly capable at tasks that follow patterns in their training data. They're unreliable at tasks that require genuine novelty, real-time information, or reasoning about things not represented in their training.
The Models That Matter (Late 2023)
| Model | Provider | Key characteristics |
|---|---|---|
| GPT-4 | OpenAI | Most capable general-purpose model. Strong reasoning, coding, analysis. Available via API and ChatGPT Plus. |
| GPT-3.5 | OpenAI | Faster and cheaper than GPT-4. Sufficient for many enterprise tasks. Good balance of capability and cost. |
| Claude 2 | Anthropic | Strong on analysis and detailed reasoning. 100K token context window (much larger than GPT-4's default). |
| PaLM 2 / Gemini | Google's model family. Powers Bard. Competitive on multilingual and reasoning tasks. | |
| Llama 2 | Meta | Open-source. Can be self-hosted for full data sovereignty. Less capable than GPT-4 but free to deploy. |
For enterprise leaders: Model choice matters less than how you build around the model. The data layer, integration, and governance determine whether your AI delivers value. The specific model is increasingly interchangeable.
What LLMs Can and Can't Do
They Can:
- Generate human-quality text - drafts, summaries, analysis, communications
- Extract structured data from unstructured documents - turning PDFs, emails, and forms into structured records
- Answer questions from provided context - RAG-based knowledge retrieval across your organisation's documents
- Code and engineering work - writing, reviewing, and explaining code
- Multilingual processing - translation, multilingual analysis, cross-language search
- Classification and routing - categorising inputs and routing them to appropriate workflows
They Can't:
- Access real-time information - they have a training cutoff and don't browse the internet (unless augmented)
- Guarantee accuracy - they hallucinate, producing confident but incorrect outputs
- Remember previous conversations - each interaction is independent (unless you build memory systems)
- Perform mathematical reasoning reliably - they approximate calculations rather than computing them
- Replace domain expertise - they amplify expertise; they don't substitute for it
Enterprise Implications
Model Selection Is Not Your Most Important Decision
The AI market is moving fast. GPT-4 is today's leader; GPT-5 is in development. Claude is improving. Open-source models are closing the gap. Any model you choose today may be superseded in 6-12 months.
This means: don't build your AI strategy around a specific model. Build your data infrastructure, integration patterns, and governance frameworks in a model-agnostic way. When better models arrive, you swap the engine. You don't rebuild the car.
Cost Structure Matters
LLMs are priced per token (roughly per word). For high-volume enterprise applications (processing thousands of documents daily, powering customer-facing tools), inference costs are a real operational expense. Architecture decisions that reduce token usage (better prompting, smaller models for simple tasks, caching) directly affect your operating costs.
Context Windows Are a Practical Constraint
LLMs can only process a limited amount of text at once. GPT-4 handles 8K-32K tokens; Claude 2 handles up to 100K. For enterprise use cases involving long documents or multiple sources, this means you need retrieval strategies that select the most relevant context. You can't just feed in everything and hope.
- Should we use GPT-4 or Claude for our enterprise AI?
- It depends on the specific use case. GPT-4 is the strongest general-purpose option. Claude 2 offers a larger context window, which is valuable for long-document analysis. For many enterprise tasks, GPT-3.5 or even open-source models are sufficient and significantly cheaper. Evaluate on your specific use case, not on benchmarks.
- Can LLMs work with our proprietary data without data leakage?
- Yes, with the right deployment. Enterprise API access (Azure OpenAI, AWS Bedrock) provides data isolation. Your data is processed but not used for training. For maximum control, open-source models can be self-hosted on your own infrastructure.
- How fast are LLMs improving?
- Very. GPT-4 (March 2023) was a significant leap over GPT-3.5 (November 2022). Expect capability improvements every 6-12 months. This is why model-agnostic architecture matters. The best model in 2024 will be different from the best model in 2023.