Dataset curation, LLM-as-judge, regression testing, OpenAI evals.

Evals — Building Evaluation Pipelines for LLM Applications

Evals (evaluations) measure how well your LLM application performs on a representative task dataset. Without evals, prompt changes and model upgrades are guesswork. Evaluation types: exact match (for structured outputs), semantic similarity (embeddings), LLM-as-judge (for quality), and human review (gold standard). A regression suite detects when prompt tweaks break existing behaviour. Tools: OpenAI Evals, LangSmith, Phoenix, and custom frameworks.

Interactive Pipeline Flow

UnderstandImplementOptimise

📊 Dataset — task + expected

Dataset: 50-200 input-expected output pairs covering important task types, edge cases, and known failure modes. Sources: production traffic, manual curation, synthetic generation.

Architecture Diagrams

Structured scoring rubric for LLM-as-judge evaluation.

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Key Design Decisions

📊Build your eval dataset before building the system

Define what success looks like before writing a single prompt. 50 high-quality input-expected pairs from real use cases are more valuable than 1000 synthetically generated ones.

🧑‍⚖️LLM-as-judge is good but not perfect

LLM judges have bias toward verbose, confident-sounding responses. Calibrate your judge with human-labeled anchor examples. Use multiple judge models and average scores for high-stakes evaluations.

🔄Evals in CI prevent regression on every change

Every prompt change, model upgrade, or retrieval modification can improve some cases while breaking others. Run your eval suite in CI and gate merges on no regression >2% in pass rate.

Discussion