Definition
Model Evaluation
Model evaluation is the practice of measuring how well an AI model or system performs against defined criteria, using test cases, scoring methods, and metrics. It replaces 'it seems to work' with evidence — catching regressions, comparing models, and verifying quality before and after deployment. For generative systems, evaluation is harder because outputs rarely have one correct answer.
Key takeaways
- Model evaluation measures how well an AI system performs against defined criteria using test cases, scoring methods, and metrics.
- It replaces 'it seems to work' with evidence — catching regressions, comparing models, and verifying quality before deployment.
- Scoring open-ended generation is the hard part: teams combine reference metrics, rubrics, LLM-as-judge, and targeted human review.
- Evaluation is continuous, not a one-time gate — an eval suite is the regression test for AI behavior as prompts, models, and data drift.
Evaluation turns AI quality into something measurable. You assemble a representative set of test inputs with known good outcomes, run the system against them, and score the results, so you can say concretely whether a change helped or hurt. Without this, prompt tweaks and model swaps are guesswork, and silent regressions ship unnoticed because a few manual spot-checks looked fine.
Scoring open-ended generation is the central challenge. Exact-match works only for narrow, structured tasks; for free-form output teams use reference-based metrics, rubric scoring, and increasingly an LLM-as-judge — a separate model grading responses against criteria. Each approach has blind spots, so robust evaluation combines automated scoring with targeted human review on the cases that matter most.
Good evaluation is continuous, not a one-time gate. As prompts, models, and data drift, an eval suite run on every change becomes the regression test for AI behavior, and production cases that fail get folded back in to harden it over time. It is the AI equivalent of a test suite for deterministic code.
Planoda's AI features are exercised against fixed evaluation cases so prompt and model changes are measured for regressions before they reach users.
Related terms
- Large Language Model (LLM)A large language model is an AI system trained on vast amounts of text to predict and generate language, enabling it to answer questions, summarize, write, and reason over natural language. LLMs power modern AI assistants and agents. They are probabilistic next-token predictors, which makes them remarkably capable but also prone to confident errors.
- Hallucination (AI)An AI hallucination is output from a language model that is fluent and confident but factually wrong, fabricated, or unsupported by the model's inputs — invented citations, fake quotes, or made-up details. It arises because models predict plausible text rather than retrieve verified facts, making hallucination an inherent risk that must be mitigated, not a bug that's simply fixed.
- Fine-TuningFine-tuning is the process of further training a pretrained language model on a smaller, task-specific dataset so it adapts to a particular domain, format, or style. Rather than training from scratch, it adjusts the existing model's weights on curated examples, producing a specialized variant that performs a narrow task more reliably than the base model.
- GuardrailsGuardrails are the controls that constrain what an AI system is allowed to do or say, enforced around the model rather than left to the model's judgment. They validate inputs and outputs, block unsafe or off-policy actions, scope which tools an agent may use, and route risky operations through approval — turning a probabilistic model into a system safe to deploy.
- InferenceInference is the act of running a trained model to produce output from new input — the moment a model is actually used, as opposed to trained. Every prompt sent to a language model triggers an inference pass. Inference dominates the running cost and latency of AI features, since it happens on every request while training happens once.