Basic Evals for Your Own Workflows

Lesson preview

Quality drifts silently as prompts, models, and inputs change, and "it seems fine" catches none of that drift. A golden set of ten real inputs with known-good outputs, scored against a simple binary rubric, turns quality from a vibe into a number you can track, defend, and improve. AI-powered workflows don't stay static — prompts get tweaked, the underlying model gets updated, and the kinds of inputs you feed it shift over time. Any one of these can quietly degrade quality without triggering an obvious alarm, because a subjective "it seems fine" impression doesn't reliably catch small, gradual regressions. Evals exist to replace that vague impression with a number. Without one, you're flying blind — you might not notice a workflow has gotten worse until a client complains, by which point you have no idea which change caused it or how long it's been broken. A golden set is simply 10 real inputs — not synthetic, not hypothetical — paired with the output you already know is correct or acceptable for each. These become your fixed benchmark: any time you want to know if a workflow still works, you run it against the same 10 inputs and compare. Building the golden set is itself valuable work, because it forces you to define, concretely, what "correct" looks like for your workflow — something many people have never actually written down, relying instead on an unexamined gut sense.

Key terms in this module

AI eval (evaluation)

An AI eval (evaluation) is a repeatable test that checks whether an AI prompt or workflow gives good answers, by running it on a fixed set of inputs and scoring the outputs against agreed criteria. It turns "seems fine" into a score you can compare.

Golden dataset

A golden dataset is a small, fixed set of real example inputs, each paired with an answer you know is right, used to test an AI system. You run every new prompt or model against it and compare the results with those known-good answers.

Regression testing for AI

Regression testing for AI is re-running a fixed set of test cases after any change to a prompt or model, to check that what worked before still works. It catches changes that raise the average score while breaking one specific case.

Frequently asked questions

Who is "Basic Evals for Your Own Workflows" for?

This Advanced module in AI Builder is for learners who already use AI daily and are ready to build their first real workflows and integrations. It takes about 14 minutes and ends with a 4-question quiz.

Do I need to code?

No traditional programming is required. You will use no-code and low-code tools, and occasionally paste a short snippet that the lesson explains line by line.

What comes before and after this module?

Before "Basic Evals for Your Own Workflows", take "Prompt Libraries & Versioning" (module 5 of AI Builder). Afterwards, continue with "Simple Guardrails".

Related modules

• Previous
  Prompt Libraries & Versioning

  Your prompts are assets. Store, name, and evolve them like one.

• Next
  Simple Guardrails

  Decide what your workflow must never do — then enforce it in three layers.

• Same topic
  The Eval Workflow

  Error analysis → judges → regression in CI. The skill clients cannot get from a demo.

• Same topic
  RAG: When It's the Right Answer

  Retrieval and generation are two systems. Evaluate them separately, or debug them never.