Fundamentals GUIDE
AI Evaluation Basics
AI evaluation tests whether a system meets a defined purpose under stated conditions.
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Overview
It combines representative examples, explicit scoring rules, and analysis of mistakes. A successful API response or a polished demonstration does not establish that the system performs the intended task reliably.
Key takeaways
- Set acceptance criteria before testing.
- Keep a held-out evaluation set.
- Measure content, workflow outcomes, and failure handling separately.
Deep Dive
Write the acceptance criteria first. Specify the input, expected output, tolerable errors, response-time constraints, and conditions that should cause the system to abstain or escalate. Include a simple baseline to show whether added complexity provides a practical benefit.
Build separate development and evaluation sets. Development examples support iteration; a held-out set tests choices after they are made. Repeatedly tuning on the final test set turns it into another development set. Record versions so a changed score can be traced to changed data, prompts, models, or scoring.
Use metrics appropriate to the task. A classifier needs class-specific error analysis; a summarizer needs checks of factual consistency and coverage; an agent needs verification of completed actions and unintended side effects. Include difficult cases rather than only typical inputs.
Review results with uncertainty and consequences in mind. A rare failure may matter more than many harmless wording differences. Repeat a stochastic task enough to understand variation, and document where the evaluation does not represent actual use. Evaluation supports a decision; it does not eliminate uncertainty.
04Worked example
Test an invoice extractor
Prepare an invented invoice with subtotal 80, tax 8, and total 88, plus another invoice where the total is absent.
Score field extraction and arithmetic consistency separately. Require an explicit missing value for the second document.
Add a case with an unrelated number near the total label to check whether the system invents a convenient answer.
What it shows
The exercise defines correctness beyond merely returning well-formed JSON.
Real-World Implementation
Test an extraction system on documents with absent and conflicting fields.
Verify an agent’s final state after an action instead of trusting its success message.
Putting it into practice
Why it matters
- Clearer decisions. It helps you separate clear technical claims from marketing language.
- Cost and budget. You can ask better implementation questions before spending money or time.
- Team and workflow. Teams with shared understanding make better product, policy, and learning decisions.
Watch out for
- Different teams may use the same term differently, so define scope early.
- Benchmarks can look strong while real-world performance is uneven.
- Ignoring data quality and evaluation plans often creates fragile outcomes.
Next steps
- Start with a plain-language definition of the outcome you need.
- Pick one success metric and one failure condition before testing.
- Run a small pilot with representative data, not a polished demo set.
- Document where AI Evaluation Basics helps and where simpler methods are better.
Sources and further reading
- scikit-learnModel selection and evaluation
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Frequently asked questions
How many test examples are enough?
There is no universal count. The required evidence depends on variability, rare failure modes, acceptable uncertainty, and the consequences of errors.
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