Testing
DeepEval brings unit testing discipline to LLM applications. Write assertions on model outputs the same way you write pytest assertions on function return values — with G-Eval metrics, faithfulness checks, and hallucination detection. This tutorial walks you from installation to CI-integrated test suite. Why LLM Applications Need Unit Tests
AI Testing
RAG applications fail at multiple points — poor chunking, irrelevant retrieval, unfaithful generation. Testing each layer independently gives you clear signal on where failures originate. This guide covers practical testing strategies for every stage of the RAG pipeline. Key Takeaways RAG has three distinct failure modes. Retrieval failure (wrong chunks returned)
AI Testing
AI agents are harder to test than static LLMs because they make sequential decisions, call tools, and can compound errors across many steps. Testing them requires a combination of unit tests for individual tools, trajectory evaluation for decision sequences, and end-to-end tests for goal completion. This guide covers
AI Testing
DeepEval, PromptFoo, and LangSmith each solve a different version of the LLM evaluation problem. DeepEval provides deep metric-based evaluation for RAG and conversational AI. PromptFoo excels at prompt comparison and red-teaming. LangSmith integrates tightly with LangChain for tracing and production monitoring. This guide breaks down when to use
AI Testing
Prompts are code — they should be versioned, tested, and reviewed like any other critical system component. This guide covers the practices that teams use to test prompts systematically before shipping changes and catch regressions as models evolve. Key Takeaways Treat prompts as first-class artifacts. Store them in version control,
AI Testing
LLM hallucinations — confidently stated falsehoods — are one of the hardest failure modes to catch because they look identical to correct outputs. This guide covers the practical techniques developers use to detect hallucinations in production systems and reduce their frequency. Key Takeaways Hallucinations are silent failures. A crashed request is easy
AI Testing
Testing LLM outputs is different from testing deterministic software. Outputs vary across runs, so you need a combination of rule-based checks, semantic similarity, and LLM-as-judge techniques. This guide walks through practical methods developers use to validate AI-generated text in production. Key Takeaways LLM outputs are probabilistic