AI-powered testing guides — how to use AI to generate, run, and maintain automated tests faster.
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
RAG evaluation frameworks exist because manual inspection doesn't scale. You can't read 10,000 (question, answer, context) triples and judge whether the model is hallucinating, whether the retrieved context is relevant, or whether the answer actually addresses the question. RAGAS and TruLens automate this judgment using
Testing
Vector databases are infrastructure. Like any database, they can be misconfigured, queried incorrectly, or updated in ways that corrupt your index. Unlike relational databases, the failure modes are subtle: incorrect metadata filters silently narrow results, wrong namespace routing returns data from a different index, stale vectors from deleted documents still
Testing
Embeddings are the foundation of semantic search, RAG systems, and recommendation engines. When embedding behavior is wrong — whether because the model changed, the preprocessing changed, or the similarity calculation is buggy — everything downstream breaks silently. Search returns irrelevant results. RAG systems hallucinate. Recommendations get weird. Testing embeddings requires thinking about
Testing
Chunking is one of the most impactful and least-tested parts of a RAG pipeline. How you split documents into chunks directly determines retrieval quality: too small and chunks lose context; too large and they include irrelevant content that confuses the LLM; wrong boundaries and questions about sentence-end information
Testing
LangChain and LlamaIndex are composable systems. A RAG chain is a sequence of components: a retriever, a prompt template, an LLM, an output parser. Each component can fail, and their integration can fail in ways that none of the individual components would catch in isolation. Integration testing for RAG chains
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