AI-powered testing guides — how to use AI to generate, run, and maintain automated tests faster.
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Weights & Biases built its reputation on ML experiment tracking — recording every hyperparameter, metric, and artifact from model training runs. Weave extends that discipline to LLM applications, where the "experiment" isn't a training run but a prompt, a retrieval config, or a pipeline change. If you&
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Every time you change a prompt, update a model version, modify your retrieval pipeline, or adjust a system instruction, you've potentially introduced a regression. Unlike traditional software regressions — where a function returns the wrong value and a unit test catches it — LLM regressions are subtle. The output format
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Most LLM applications fail at the infrastructure layer, not the model layer. Your prompt is fine. Your retrieval is fine. But when OpenAI's API goes down at 2 AM, or when your token budget runs out mid-sprint, the application breaks entirely. Portkey is an AI gateway that
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LLM applications fail in ways that traditional monitoring misses entirely. A REST API either returns 200 or it doesn't. An LLM can return 200 and still produce hallucinated facts, broken reasoning chains, or responses that drift from your prompt instructions over time. Arize Phoenix exists to make that
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Building applications on top of the OpenAI API — GPT-4, GPT-4o, embeddings, DALL-E — requires a solid test strategy. LLM outputs are non-deterministic, calls cost money, and rate limits punish naive test suites. This guide gives you a complete Python testing framework for OpenAI-powered applications. The Core
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Testing LLM-powered applications is one of the most misunderstood areas of software quality. Traditional testing relies on determinism — the same input always produces the same output. LLMs don't work that way. This guide covers the patterns that actually work. The Core Challenge: Testing Non-Determinism When you
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Every production application that calls OpenAI, Anthropic, or Google Gemini will eventually hit a rate limit. The question isn't whether you'll get a 429 error — it's whether your application handles it gracefully. Here's how to test every aspect of rate limit handling
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Calling the real OpenAI, Claude, or Gemini API in every test is slow, expensive, and non-deterministic. Mocking replaces those API calls with controlled, predictable responses — making your unit tests fast and your CI pipeline cheap. Here's every mocking pattern you need. Why Mock LLM APIs Before diving
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Google's Gemini API powers everything from simple text generation to multimodal applications that process images, audio, and documents. Testing Gemini-powered apps requires handling non-deterministic outputs, managing API costs, and validating complex multimodal behaviors. Here's a complete testing framework. Gemini SDK Overview Google provides two
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Testing AI APIs requires a different mindset than testing traditional REST endpoints. The Claude API from Anthropic returns probabilistic outputs — the same input can yield different responses. This guide shows you how to build a reliable test suite for applications built on the Claude API. Why Claude API Testing Is
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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.
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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.