AI-powered testing guides — how to use AI to generate, run, and maintain automated tests faster.
Multimodal AI
Vision-language models changed the product surface for AI applications. GPT-4V, Gemini Vision, Claude Vision, and LLaVA can describe images, read documents, analyze charts, and answer questions about visual content. They're appearing in production features — receipt extraction, UI accessibility checking, content moderation, medical image description — and they need tests.
Speech Testing
Speech AI systems — transcription APIs, TTS engines, voice assistants — produce outputs that don't fit a binary pass/fail test. The question is never "did it produce output" but "is the output good enough." Answering that question consistently, automatically, and in CI requires evaluation frameworks
Testing
Vector databases power the retrieval layer of AI applications — semantic search, RAG pipelines, recommendation systems, and knowledge bases. But testing them is different from testing SQL databases. You can't assert on exact row matches; you're asserting on approximate similarity, ranking order, and threshold behavior. This guide
Testing
Pinecone is a managed vector database used in production RAG systems, semantic search, and recommendation engines. Testing Pinecone integrations differs from testing local databases — you're working with a remote, managed service, which introduces network latency, eventual consistency, and cost considerations. This guide covers integration testing strategies for Pinecone:
Testing
Embedding models are the foundation of vector search, RAG pipelines, and semantic applications. When you switch embedding models, update to a new version, or change preprocessing, the entire downstream system is affected. Without a testing strategy, you may not notice degraded retrieval quality until users complain. This guide covers testing
Testing
RAG (Retrieval-Augmented Generation) pipelines combine a retrieval system with an LLM to answer questions using your own data. Testing RAG pipelines is challenging because both the retrieval component and the generation component can fail independently — or fail together in ways that aren't obvious. This guide covers testing RAG
Testing
ChromaDB is one of the most popular open-source vector databases for AI applications — lightweight, easy to embed in Python applications, and well-suited for testing because it runs fully in-memory without external dependencies. This guide covers testing ChromaDB integrations: collection management, embedding functions, metadata filtering, and persistence. Why ChromaDB Is Testing-Friendly
LLM Testing
LLM applications introduce a new category of failure that traditional testing tools were never built to catch. A response that was accurate yesterday might drift subtly today — same prompt, different model behavior. Tracing, evaluation datasets, and online scoring are how production teams stay ahead of that drift. Langfuse is one
AI Testing
AI chatbots are complex systems that can fail in ways traditional software doesn't: context drift across turns, persona inconsistency, unhandled edge cases, and graceful degradation when the LLM produces unexpected output. End-to-end chatbot testing requires covering all these dimensions — not just "does it respond?" but "
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 upgrade improves or degrades your application&
AI Testing
AI applications are only as safe as their guardrails. Prompt injection, jailbreak attempts, and content filter bypass are real attack vectors against production LLM applications. Testing your guardrails — systematically and automatically — is as important as testing your authentication or input validation. This guide covers how to build a guardrail test
AI Testing
LlamaIndex is a popular framework for building RAG (Retrieval-Augmented Generation) pipelines. Testing these pipelines requires verifying three distinct components: the retriever (is it finding relevant documents?), the context (is the retrieved content accurate?), and the generator (is the answer faithful to the context and free of hallucination?). This guide covers