AI Evaluation
AI evaluation is the practice of systematically measuring how well an AI system performs against defined criteria—accuracy, latency, cost, safety, user satisfaction—before and after deployment. This sounds obvious. It is not widely practiced. Most organizations deploying AI in 2025 evaluate by vibes: someone runs a few test queries, the results look reasonable, and the system ships. Rigorous evaluation requires test datasets that represent real usage, metrics that map to business outcomes (not just model benchmarks), and automated pipelines that run evaluations on every change. The gap between "works in a demo" and "works reliably in production" is almost entirely an evaluation gap. Without good evals, you cannot tell whether a prompt change, model upgrade, or architecture tweak made things better or worse. You are flying blind and calling it agile.
Related terms:
Temperature
Temperature is a parameter controlling a language model’s randomness: at 0 it always picks the most probable next token for deterministic, reliable output, at 1 it samples more broadly for varied, creative results, and above 1 it becomes increasingly random. Choosing the right temperature (e.g., 0 for consistent data extraction or 0.7–0.9 for brainstorming) balances reliability and diversity.
Generative AI
Generative AI refers to AI systems that learn statistical patterns from training data to create new content—such as text, images, code, audio, or video—rather than classifying or analyzing existing data. This marks a shift from earlier discriminative models like spam filters and recommendation engines, with tools like ChatGPT, DALL-E, Midjourney, and Stable Diffusion driving its rapid mainstream adoption.
Token
In large language models, a token is the basic unit of text—usually chunks of three to four characters—that the model reads and generates. Since API costs, context windows, and rate limits are all measured in tokens, understanding tokenization is essential for controlling prompt length, cost, and model behavior.