What It Is
Built on the transformer architecture, an LLM can answer questions, summarize documents, translate, and write code. Anthropic’s Claude models are well-known examples. The model learns patterns of words and concepts from training data, then predicts likely text in response to a prompt.
Key Points
- Hallucinations: outputs may be fluent but wrong.
- Knowledge cutoff: limited without connection to external data.
- Prompt sensitivity: wording affects results.
- Token cost: cost scales with the amount of text processed.
Why It Matters
Results improve with RAG, fine-tuning, tools, and guardrails rather than relying on the base model alone. Costs also scale with tokens processed, so efficient prompt design and model choice matter in production.
How ClearLeaff Applies It
As a certified Anthropic Claude registered partner, we design, optimize, and deploy systems using Sonnet, Haiku, and Opus, covering agentic reasoning, prompt caching, retrieval pipelines, and LLMOps.