AI Testing
Deploying an LLM-powered application without safety testing is shipping code without security review. The risks are concrete: users manipulating your model to bypass restrictions, prompt injection attacks that hijack agent behavior, unintended leakage of system prompts or training data, and content policy violations that create legal or reputational exposure.
AI Testing
The most common source of production failures in LLM applications isn't a bad answer — it's a bad format. Your code expects {"sentiment": "positive", "score": 0.87} and the model returns The sentiment is positive with a confidence of 87%. JSON.
AI Testing
Measuring the quality of a large language model output is fundamentally different from measuring traditional software behavior. There is no single assert response == expected — you are comparing probabilistic text against a range of acceptable answers. Getting this right requires a layered approach: automated metrics for scale, model-based judges for
AI Testing
When a model release announces "state-of-the-art on MMLU" or "beats GPT-4 on HumanEval," how much should you care? Understanding what these benchmarks actually measure — and what they don't — is essential for making informed model selection decisions and designing your own
AI Testing
"The new prompt feels better" is not an evaluation strategy. Moving from GPT-4 to Claude, or changing a system prompt, requires rigorous A/B testing to make confident decisions — especially when the differences in quality are subtle and user impact is significant. This guide covers the statistical
AI Testing
Deploying an LLM is not a one-time event. Prompts change. Models get updated. Retrieval indexes get refreshed. Each of these changes can silently degrade the quality of your AI application — and without a continuous evaluation pipeline, you won't know until users start complaining. This guide covers how
MLOps
OpenAI Evals is a framework for evaluating large language model outputs. It lets you define test cases (called "evals") that measure whether a model produces correct, consistent, or appropriately formatted responses — and run them as automated checks in your development pipeline. What Evals Test Evals measure properties of
AI Testing
Multi-agent systems do not fail the way ordinary software fails. A single misrouted request, a tool that returns an unexpected shape, or a termination condition that never fires can cascade into hours of wasted compute and completely wrong outputs — with no stack trace to show for it. Testing them
AI Testing
LLM providers update models silently or with minor version bumps — and your application's behavior can change significantly without any code change. GPT-4o-2025-04 may behave differently from GPT-4o-2025-01. LLM regression testing gives you baselines to compare against, so you know when a model
AI Testing
LLM applications fail in ways that traditional software doesn't: hallucination, context drift, prompt injection, and quality regression between model versions. Evaluating these requires specialized frameworks that go beyond unit tests. RAGAS, DeepEval, PromptFoo, and Langfuse each take a different approach. This guide compares them so you can pick
Testing
Retrieval-Augmented Generation (RAG) systems are notoriously hard to test. Unlike deterministic software where the same input always produces the same output, RAG pipelines combine a retrieval step (vector search, keyword search, or hybrid) with an LLM generation step — both of which introduce variability. A RAG system can appear to
Testing
LLM regressions are silent killers: the app still returns a response, but the quality degraded after a prompt change, model upgrade, or context window modification. Traditional regression tests that assert exact string equality fail immediately with LLMs. This guide covers behavioral regression testing, prompt versioning, LLM-as-judge evaluation, and