AI/ML6 min read

AI Model Evaluation

Design evaluation suites to measure LLM accuracy, faithfulness, and task performance - catching regressions before they reach production.

Model EvaluationLLMBenchmarksRAGASEvals

skill: AI Model Evaluation category: AI/ML tags: [Model Evaluation, LLM, Benchmarks, RAGAS, Evals] readingTime: 6

Evaluation is the most neglected part of AI development and the most important. Without evals, you don't know if a prompt change made things better or worse. Start with a small golden dataset (50-100 hand-labeled examples) before any model change. Automated metrics alone are not enough - include human spot-checks.

Workflow

Define what 'good' looks likeAn eval without a clear success criterion measures noise. Define the task, acceptable outputs, and failure cases before writing code.
Build a golden datasetA curated set of inputs + expected outputs is your eval's ground truth. Collect real examples from production (sanitized) - they reflect actual edge cases.
Run automated metricsAutomated metrics catch obvious regressions fast and cheaply - human review is for the 10% cases automated metrics miss.
Use LLM-as-judge for open-ended qualityMany tasks (helpfulness, tone, creativity) can't be measured with string-matching metrics. An LLM judge evaluates these holistically at scale.
Integrate evals into CI/CDEvals that only run manually will be skipped under deadline pressure. A CI gate prevents regressions from merging.

Output Format

Eval suite: golden dataset, automated metrics, LLM-as-judge scores, and CI gate with regression threshold.

eval_suite/
├── golden_dataset.jsonl    # Hand-labeled examples
├── run_evals.py            # Scoring script
├── metrics/
│   ├── rouge.py
│   ├── exact_match.py
│   └── llm_judge.py
└── results/
    └── 2026-08-05.json     # Score history

Examples

Input

Output

Edge Cases & Gotchas

LLM judge self-preference - GPT-4 tends to prefer GPT-4-style outputs over others (positional bias, verbosity bias). Randomize response order and use multiple judges.
Metric gaming - Optimizing ROUGE can produce outputs that match reference tokens but are grammatically broken. Always pair automated metrics with human spot-checks.
Distribution shift - Your golden dataset reflects past examples. If user behavior changes, evals may pass while production degrades. Add production sample logging and weekly golden set updates.
Flaky evals from temperature - Temperature > 0 produces different outputs each run. Either set temperature=0 for evals, or run each example 3x and average.

When to Break These Rules

Don't use eval scores as the only gate for safety-critical decisions - human review of failure cases is non-negotiable for medical, legal, or financial AI.
Don't over-engineer evals before you have a working product - a 20-example smoke test on main happy paths is better than no evals at all.

Reference Files

https://github.com/openai/evals
https://docs.ragas.io/