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Evaluate LLM output without a PhD

You do not need a research setup to know whether your AI feature works. A spreadsheet, twenty real examples and one honest rubric will get you far.

Agenoverse Team

1 min read

"It seems to work" is how most AI features ship, and how most of them break. A basic evaluation takes an afternoon and pays for itself the first time you change a prompt.

Collect real examples#

Pull twenty real inputs from your product (or the ones you expect). Write what a good answer looks like for each, in a sentence. Put them in a spreadsheet.

Write a rubric you can apply#

Pick three to five yes/no questions: Is it correct? Does it follow the format? Does it stay in scope? Yes/no is easier to agree on than a 1-to-10 score.

evaluate.ts
const results = await Promise.all(  examples.map(async (example) => {    const output = await generate(example.input)    return { id: example.id, output, passed: rubric(example, output) }  }))console.log(`${results.filter((r) => r.passed).length}/${results.length} passed`)

Run it on every change#

The number itself matters less than the trend. When it drops after a change, you have found the problem before your users did.

Note

Add a new example every time something fails in production. Your test set slowly becomes a record of everything you have learned.

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