01Step 01
Golden Dataset
Curate 50-200 representative input/output pairs that cover your key use cases, edge cases, and failure modes. This is your ground truth for every evaluation cycle.
HowtoEvaluate&TestLLMApplications
Youcan'timprovewhatyoucan'tmeasure.Here'sapracticalframeworkforevaluatingLLMoutputsandbuildingconfidenceinyourAIsystembeforeandafterdeployment.
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The Evaluation Problem
2 paragraphs
LLM outputs are non-deterministic, subjective, and context-dependent. Traditional software testing (assert expected === actual) doesn't work. You need a different evaluation framework, one that embraces ambiguity while still catching regressions.
The biggest mistake teams make is shipping LLM features without any evaluation framework. The second biggest is over-investing in complex metrics before establishing basic quality baselines.
The Breakdown05 Items
Evaluation Framework
02Step 02
Automated Metrics
Use LLM-as-judge (GPT-4 scoring outputs on relevance, accuracy, tone), semantic similarity, ROUGE/BLEU for summarization, and custom rubrics for domain-specific quality dimensions.
03Step 03
Human Evaluation
Regular human review of random production outputs, rating quality, flagging failures, and identifying patterns that automated metrics miss. This is the calibration layer.
04Step 04
Regression Testing
Run your golden dataset against every prompt change, model update, or system modification. If scores drop, investigate before deploying. Treat prompt changes like code changes.
05Step 05
Production Monitoring
Track latency, token usage, error rates, user feedback signals (thumbs up/down, regeneration rate), and content safety flags. Set alerts for anomalies.
Tools & Implementation
2 paragraphs
LangSmith, Braintrust, and Promptfoo are purpose-built for LLM evaluation. For simpler setups, a spreadsheet of test cases with a Python script running evaluations is often enough to start.
The key insight: evaluation is not a one-time activity. It's a continuous process that runs on every change. Build it into your CI/CD pipeline from the beginning, since it's much harder to add retroactively.
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