What Are Evals?
Evaluations (Evals) are systematic assessments designed to measure the performance, reliability, and accuracy of Generative AI (GenAI) applications. They help prompt engineers and AI teams optimize their prompts, models, and responses to ensure alignment with business objectives and user expectations.
By leveraging Evals, teams can continuously refine their AI applications, improve response quality, and identify potential failure points before deploying models into production.
Types of Evals
Evals can be broadly categorized into three main types:
Human-in-the-Loop (HITL) Evals: Manual assessments where human reviewers rate model outputs for quality, consistency, and effectiveness.
Automated Evals: Predefined tests that assess model performance based on structured criteria such as accuracy, relevance, and coherence. These can either be deterministic-based Evals (using code with regular expressions for example) or LLM-based Evals.
LLM-as-a-Judge (Custom Evals): AI-driven evaluations where another language model (LLM) assesses responses using predefined scoring mechanisms or heuristics.
Each evaluation type has its place depending on the complexity of the task, the stage of development, and the required level of accuracy.