Guides / Evaluation

Evaluating AI Systems

A systematic approach to measuring AI quality, reliability, and safety before and after deployment.

10 min read

Getting Started Prompt Engineering RAG Fine-Tuning AI Agents Evaluation Deployment

Define your metrics first

Before running evals: write down what "good" looks like. For Turkish NLP: exact match, ROUGE-L, BERTScore-Turkish, and human preference ratings.

Build a golden dataset

200–500 human-verified examples covering edge cases. Refresh quarterly. Never reuse train data in eval — contamination inflates scores by 10–20%.

Automated vs. human eval

Automate: accuracy, format compliance, latency, cost. Humanise: tone, cultural appropriateness, Turkish naturalness. Run human eval on 10% of outputs monthly.

Regression testing

Every prompt change, model update, or dependency bump triggers the eval suite. Gate deployment on <2% accuracy regression and <20ms P95 latency increase.

Eval for safety

Test for: prompt injection, jailbreaks, PII leakage, Turkish hate speech generation. Use adversarial examples. Run red-team monthly before major releases.