How to build an AI eval suite for a production LLM app

Most teams ship LLM features without a repeatable way to measure them. An eval suite fixes that. Here's how to build one from scratch.

Step 1: Write down the job

Before you touch code, describe the task in one sentence: what input goes in, what output should come out, and what a bad answer looks like. Vague jobs produce vague evals.

Step 2: Collect a starter dataset

Pull 30–100 real examples from logs, support tickets, or subject-matter experts. Include:

• Happy path cases — the common asks.
• Edge cases — long inputs, ambiguous asks, missing fields.
• Adversarial cases — prompt injections, off-topic asks, jailbreak attempts.

Step 3: Define the grading standard

For each example, decide how you'll score the output. Options:

• A gold reference answer plus a similarity or exact-match check.
• A rubric with 3–5 named criteria (accuracy, tone, safety, format).
• A pass/fail rule the output must satisfy.

Step 4: Pick your graders

Mix three types:

1. Code checks for anything deterministic — JSON schema, forbidden words, response length.
2. LLM-as-judge for rubric scoring at scale. Version the judge prompt.
3. Human review for a sampled slice each week — this keeps the automated graders honest.

Step 5: Wire it into CI and production

Run the suite on every prompt change, model swap, or retrieval update. Track pass rate, cost per run, and p95 latency over time. In production, sample live traffic and grade it against the same rubric so drift shows up before users complain.

Step 6: Iterate the eval, not just the model

When users hit a failure your suite missed, add that case. Evals are a living asset — they get sharper every time the system surprises you.

Silverberry builds this discipline into every engagement through our Responsible AI-by-Design Framework and AI Consulting Services.