Evaluation & Quality

Traditional software testing gives you deterministic pass/fail. AI evaluation is fundamentally different: outputs are non-deterministic, quality is subjective, and production data drifts away from your test set. This article reframes your mental model from 'testing' to 'measurement science.'

Not everything that can be measured matters, and not everything that matters can be easily measured. This article builds a framework for selecting the right evaluation metrics for your specific AI system — from task completion and faithfulness to operational metrics like cost and latency.

Your evaluation is only as good as your dataset. This article covers the complete lifecycle of building eval datasets: manually curating golden sets, generating synthetic data with stronger models, crafting adversarial examples, avoiding contamination, and determining the right dataset size for statistical significance.

Most AI teams make deployment decisions based on 'it feels better.' This article equips you with the statistical tools to make defensible claims: confidence intervals, hypothesis testing, bootstrap methods, and variance reduction for non-deterministic AI systems.

Using one LLM to evaluate another's output is the most scalable automated evaluation technique available. This article covers the complete LLM-as-judge pipeline: rubric design, position bias mitigation, calibration against human judgment, judge model selection, and production-ready implementation.

Should you compare outputs against a gold standard, or evaluate them in isolation? This article covers both paradigms: traditional reference-based metrics (BLEU, ROUGE, BERTScore), reference-free model-based evaluation, and when to use each approach — with practical code for both.

Generic evaluation metrics miss domain-specific quality. Code generation needs execution tests. Summarization needs faithfulness checks. Classification needs confusion matrices. This article builds custom evaluators for the most common AI application domains, with production-ready code for each.

Evaluation is not a one-time event — it is a continuous pipeline that runs on every prompt change, gates deployments, detects regressions, and monitors production quality. This article builds the complete CI/CD evaluation pipeline: from GitHub Actions integration to quality gates, regression detection, and production monitoring with alerting.

Run LLM-as-judge and code evaluators on production traces in real-time — catch quality regressions, monitor safety, and score every response without slowing down users.

LangSmith's pytest plugin and Vitest/Jest integration bring LLM evaluation into your CI/CD pipeline — with fuzzy matching, embedding distance, test caching, and rich terminal output.

Automated evaluation scales but misses nuance. Human evaluation catches what LLM judges cannot — subtle quality issues, user experience problems, and domain-specific errors. This article covers the complete design of human evaluation: annotation guidelines, inter-annotator agreement, quality control, scaling strategies, and cost management.

Offline evaluation tells you a system should be better. A/B testing tells you it actually is better for real users. This article covers experiment design for AI features: proper randomization, choosing metrics that matter, guardrail metrics that must not regress, sample size planning, and common pitfalls that invalidate results.

Users interact with your AI system thousands of times per day. Every interaction contains a signal about quality — if you know how to capture it. This article covers explicit feedback (thumbs up/down, ratings, corrections), implicit feedback (retry behavior, session patterns), turning feedback into improvement, and avoiding feedback fatigue.

Red-teaming is the practice of deliberately trying to break your AI system before attackers do. This article covers systematic red-teaming: attack taxonomies, prompt injection techniques, automated attack generation, defense validation, and building a red-teaming pipeline that runs continuously as part of your evaluation suite.

Compare two outputs side-by-side with programmatic pairwise evaluators or human Pairwise Annotation Queues (PAQs) — the most reliable way to judge which version is better.

Evaluating an agent on its final answer misses most of the story. An agent that stumbles through 15 wrong steps before reaching the right answer is not the same as one that reaches it in 3 clean steps. This article covers trajectory evaluation: scoring the reasoning path, measuring efficiency, evaluating decision quality at each step, and tracking cost per trajectory.

Agents interact with the world through tools. Evaluating tool use means checking whether the agent selected the right tool, passed correct arguments, called tools in the right order, and handled errors gracefully. This article builds a complete tool use evaluator with per-step scoring and production-relevant examples.

Single-turn evaluation misses a critical reality: most AI interactions are conversations. Multi-turn evaluation measures how well a system maintains coherence across turns, uses previous context effectively, degrades gracefully over long conversations, and recovers from errors. This article builds a multi-turn eval harness with per-turn and aggregate scoring.

RAG systems have two failure points: retrieval can return wrong documents, and generation can hallucinate despite correct documents. Evaluating RAG quality requires separating these concerns: measure retrieval quality independently, measure generation quality independently, then measure end-to-end correctness. This article builds a complete RAG evaluation pipeline with metrics from the RAGAS framework and custom scoring.

The agentevals package provides formal trajectory evaluation with four match modes (strict, unordered, subset, superset) and LLM-as-judge trajectory scoring.