AI in practice

Not every problem needs the biggest model. Model selection is about matching capability to requirements: a 7B-parameter model might solve your classification task perfectly at 1/100th the cost of a frontier model. The key dimensions are capability (can it do the task?), latency (how fast?), cost (per-token pricing), and control (can you host it? fine-tune it?).

Frontier models (GPT-4o, Claude Opus, Gemini Ultra) excel at complex reasoning, long-context tasks, and following nuanced instructions — but you pay a premium. Smaller open models (Llama 3, Mistral, Phi) are 10–100× cheaper per token and can run locally, making them ideal for high-volume, narrowly-scoped tasks.

Benchmarks provide standardized tests for comparing LLMs, but they have limits: models can be trained on benchmark data (contamination), benchmarks may not reflect your actual use case, and aggregate scores hide important failure modes. The key benchmarks are MMLU (57-subject knowledge test), HumanEval (code generation), GSM8K (grade-school math), and Chatbot Arena (human preference via blind A/B voting).

For production, you need your own eval suite — a set of representative inputs with expected outputs, scored by an automated metric (exact match, BLEU, ROUGE) or, increasingly, by another LLM acting as judge (LLM-as-judge). This catches regressions when you swap models, update prompts, or add RAG pipelines.