AI Evaluation (Evals)

What is AI evaluation (evals)?

AI evaluation, usually shortened to evals, is how teams find out whether an AI feature actually works. Traditional software tests check that the same input gives the same output. AI output varies from run to run, so evals score it instead: is the answer correct, grounded in the right source, in the expected format, safe and useful?

An eval has three parts. A dataset of realistic inputs, often drawn from real user questions and edge cases. A definition of a good result for each, such as a reference answer, required facts or a scoring rubric. And a grader, which can be code that checks exact values, another model acting as a judge, or a human reviewer. Run the system over the dataset and you get a score you can compare between versions.

Evals matter anywhere a business ships features built on a large language model: support assistants, search, document extraction, classification and agents.

Why do AI evals matter?

Without evals, teams judge AI features by trying a few prompts and deciding the results look fine. That misses the long tail of questions real users ask, and it gives no way to tell whether a change made things better or worse.

Evals turn quality into something you can track. When you edit a prompt, swap models, change retrieval or add documents, you rerun the suite and see which cases improved and which broke. That makes it safer to move quickly, because regressions show up before users find them. Evals also inform cost decisions: if a smaller, cheaper model scores the same on your tasks, you have evidence to switch. And they give product owners a shared definition of good enough, which otherwise is left to opinion.

What types of AI evals are there?

Most teams combine several kinds, because each catches different problems:

• Code-based checks: exact matches, valid JSON, required fields present, figures that match a source system. Fast and cheap, and best for structured outputs.
• Model-graded evals: a second model scores answers against a rubric, such as faithfulness to sources or tone. Scales well but needs spot checks against human judgment.
• Human review: domain experts grade a sample. Slower, but the reference point for everything else.
• Online evals: signals from production, such as user ratings, escalations or edits to AI drafts.

Use code-based checks wherever the output allows them, and add model grading and human review for the qualities code cannot judge. For retrieval-augmented generation (RAG) systems, score retrieval and answer quality separately, so you know which part failed.

How do you build an eval set?

Start small and real. Collect inputs from logs, support tickets or user interviews rather than inventing them, and include the awkward ones: ambiguous questions, missing information, out-of-scope requests and cases where the right answer is to decline.

For each input, write down what a good answer must contain or must avoid. Keep the criteria specific enough that two reviewers would agree. Then grow the set over time: every bug report, wrong answer or complaint becomes a new case, so the same mistake cannot return unnoticed.

Keep the eval set separate from the examples used in prompts or fine-tuning, otherwise scores will look better than real performance. Version the dataset alongside the code, and run it automatically before each release.

How do you know when an AI feature is good enough to ship?

There is no universal score. The bar depends on what an error costs. A drafting assistant whose output a person edits can ship at a lower score than a feature that answers customers about billing or writes data into a system of record.

A practical approach is to agree the bar before building: which cases must always pass, which failure types are unacceptable, for example hallucinated prices or leaked personal data, and what overall quality is acceptable on the rest. Pair the offline score with a limited launch, such as an internal pilot or a small share of traffic, and watch production signals. If they agree with the eval results, widen the rollout. If they disagree, the eval set is missing something, and fixing that comes before scaling.

How does Vizio AI use evals when building AI features?

When our Venture Studio builds AI-enabled product features, whether inside an MVP or an existing SaaS platform, evals are part of the build rather than an afterthought. We define what a good result looks like with the product owner, assemble test cases from real or realistic inputs, and use the scores to choose between prompts, models and retrieval approaches.

After launch, the growth and iteration systems we set up feed real usage back into the eval set, so each change to the feature is checked against what users actually ask. If you are still validating an idea, the practical start is a small set of cases that defines success for the feature before any code is written.