AI-powered testing guides — how to use AI to generate, run, and maintain automated tests faster.
AI Testing
Eval-driven development (EDD) applies the discipline of TDD to LLM application development. Instead of writing unit tests for deterministic code, you write evals that define what "good" looks like for your AI — before building the feature. Failing evals are your spec. This guide covers how to structure
AI Testing
CrewAI's abstraction is clean: you define agents with roles and tools, wire them into tasks, and the crew handles delegation. What's less clean is testing it. A crew.kickoff() call can trigger a chain of LLM calls, tool invocations, and agent-to-agent handoffs that costs
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
GroupChat is where AutoGen gets genuinely powerful — and genuinely unpredictable. You have a manager agent directing a conversation between specialists, a speaker selection policy deciding who talks next, and a termination condition that needs to fire at exactly the right moment. Any one of those can go wrong in ways
AI Testing
Most LangGraph developers test their graphs end-to-end: compile the graph, call .invoke(), assert on the final output. This works until something goes wrong. When a multi-hop workflow produces a wrong answer, you have no idea which node introduced the error, whether it was a routing decision or
AI Testing
Evaluating a traditional machine learning model is a solved problem. You have a test set, a metric, and a number. Evaluating an agentic AI pipeline is not solved, and the gap between the two is larger than most teams expect when they first try to apply standard ML evaluation thinking
AI Testing
A golden dataset is a curated set of inputs with known-good outputs (or evaluation criteria) used to measure whether your LLM application is working correctly. Without one, you're evaluating against vibes. This guide covers how to build, annotate, version, and maintain golden datasets for LLM applications — from
AI Testing
Testing LLM applications is hard because the outputs are open-ended text — there's no single correct answer. LLM-as-judge sidesteps this by using a capable model to score another model's outputs according to defined rubrics. This guide covers the key evaluation patterns: G-Eval for
Voice AI
Voice AI has moved from novelty to infrastructure. Transcription powers meeting summarization, accessibility tooling, and customer support automation. Text-to-speech drives voice assistants, audiobooks, and IVR systems. Both are production-critical, and both break in ways that traditional QA has no framework for handling. This guide covers how to
Whisper
OpenAI's Whisper and AssemblyAI are the two most common transcription APIs in production Python applications. They have different architectures, pricing models, and accuracy profiles — but they share the same testing challenges. This guide walks through a complete test suite for both, covering unit tests, integration tests, and edge
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
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