What are AI evals?

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What do AI evals measure?
Good evals score the things that matter to the business
Accuracy: How often the answer is correct.
Task success: Did the AI actually complete the job?
Hallucinations: Did it make things up or state false facts?
Retrieval quality: Did it pull the right documents or facts?
Format & consistency: Did it follow the required structure every time?
Cost & speed: Is it efficient enough to use at scale?
A good eval measures what success really looks like.
If you do not define the score, you cannot manage the result.

How AI evals work
Define the task, run tests, score results, compare versions
Pick a task: Example: summarize a service email, answer a policy question, or route a support ticket.
Build test cases: Create example inputs and note the expected answer or outcome.
Run the system: Test different prompts, models, or workflows on the same cases.
Score the output: Measure quality, compare results, and keep the best version.
Evals turn opinions into repeatable comparisons.
Same task. Same test set. Clearer decisions.

Types of AI evals
Different tests answer different quality questions.
Automated checks: Useful when there is a clear right answer, such as classification or extraction.
Model-graded evals: Another model scores helpfulness, clarity, or quality using set criteria.
Human review: People judge nuance, tone, edge cases, and business usefulness.
Production monitoring: Track live quality after launch to catch drift, failures, or new patterns.
The best eval strategy often uses more than one type.
Use the simplest eval that still tells you the truth.

Why AI evals matter
Measure first so you do not deploy guesswork
Catch failures early: Find weak prompts, bad retrieval, or broken workflows before users do.
Compare options fairly: See which model, prompt, or workflow actually performs better.
Reduce risk: Limit hallucinations, compliance issues, and poor customer experiences.
Prove improvement: Show whether changes increased quality, speed, or task success.
Build trust: Teams adopt AI faster when there is evidence it works.
What gets measured can be improved.
A system without evals is harder to trust and harder to scale.

The small-business takeaway
Start simple: test one real workflow before you scale.
Pick one real business task: Choose something repetitive such as email triage, FAQ answers, document search, or routing.
Collect 20 to 50 examples: Use real cases and note what a good result looks like.
Set your score: Track task success, correctness, or time saved.
Compare versions: Test prompts, models, or workflows against the same cases.
Deploy the winner and keep checking: Monitor performance as your data and business needs change.
Small evals create smarter AI decisions.
You do not need a giant benchmark to start, just a real workflow and a fair test.

Lesson done.

Up next

What is a golden set?

Where does this fit your business? Get your AI plan

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The short answer

AI evals are structured tests that measure how well an AI system does a specific job, by running real example inputs and scoring the answers against what is correct. For a small business, they replace guessing with evidence: try one real task on 20 to 50 examples before you rely on it.

Three things to remember

01Evals score real example inputs against expected answers, so you compare versions fairly instead of trusting opinions.
02Good evals measure what success means for you: correct answers, finished jobs, no invented facts, cost and speed.
03Start with one repetitive task and 20 to 50 real examples, then keep checking after launch.

Every slide, in words

01
What are AI evals?
Testing whether an AI system actually does the job well
AI evals are structured tests used to measure how well a model or workflow performs on a task.
Task: A real goal or question for the AI.
Test cases: A set of example inputs with expected answers.
Scored results: Measure how well the AI's answers match what's correct.
Evals check performance with real tests instead of guessing whether the system is good. This shows what it does well and where it needs to improve.
If you do not measure it, you do not really know if the AI works.
02
What do AI evals measure?
Good evals score the things that matter to the business
Accuracy: How often the answer is correct.
Task success: Did the AI actually complete the job?
Hallucinations: Did it make things up or state false facts?
Retrieval quality: Did it pull the right documents or facts?
Format & consistency: Did it follow the required structure every time?
Cost & speed: Is it efficient enough to use at scale?
A good eval measures what success really looks like.
If you do not define the score, you cannot manage the result.
03
How AI evals work
Define the task, run tests, score results, compare versions
Pick a task: Example: summarize a service email, answer a policy question, or route a support ticket.
Build test cases: Create example inputs and note the expected answer or outcome.
Run the system: Test different prompts, models, or workflows on the same cases.
Score the output: Measure quality, compare results, and keep the best version.
Evals turn opinions into repeatable comparisons.
Same task. Same test set. Clearer decisions.
04
Types of AI evals
Different tests answer different quality questions.
Automated checks: Useful when there is a clear right answer, such as classification or extraction.
Model-graded evals: Another model scores helpfulness, clarity, or quality using set criteria.
Human review: People judge nuance, tone, edge cases, and business usefulness.
Production monitoring: Track live quality after launch to catch drift, failures, or new patterns.
The best eval strategy often uses more than one type.
Use the simplest eval that still tells you the truth.
05
Why AI evals matter
Measure first so you do not deploy guesswork
Catch failures early: Find weak prompts, bad retrieval, or broken workflows before users do.
Compare options fairly: See which model, prompt, or workflow actually performs better.
Reduce risk: Limit hallucinations, compliance issues, and poor customer experiences.
Prove improvement: Show whether changes increased quality, speed, or task success.
Build trust: Teams adopt AI faster when there is evidence it works.
What gets measured can be improved.
A system without evals is harder to trust and harder to scale.
06
The small-business takeaway
Start simple: test one real workflow before you scale.
Pick one real business task: Choose something repetitive such as email triage, FAQ answers, document search, or routing.
Collect 20 to 50 examples: Use real cases and note what a good result looks like.
Set your score: Track task success, correctness, or time saved.
Compare versions: Test prompts, models, or workflows against the same cases.
Deploy the winner and keep checking: Monitor performance as your data and business needs change.
Small evals create smarter AI decisions.
You do not need a giant benchmark to start, just a real workflow and a fair test.

More in part 9

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