AI Testing

AI Testing

OpenAI API Testing Guide: Automate GPT-4 Tests with Python

Building applications on top of the OpenAI API — GPT-4, GPT-4o, embeddings, DALL-E — requires a solid test strategy.

AI Testing

LLM API Testing Best Practices: How to Test AI Outputs Reliably

Testing LLM-powered applications is one of the most misunderstood areas of software quality.

AI Testing

AI API Rate Limit Testing: Test Retry Logic and Exponential Backoff

Every production application that calls OpenAI, Anthropic, or Google Gemini will eventually hit a rate limit.

AI Testing

How to Mock LLM APIs in Tests: Mock OpenAI, Claude, and Gemini

Calling the real OpenAI, Claude, or Gemini API in every test is slow, expensive, and non-deterministic.

AI Testing

Gemini API Testing Guide: Test Google AI with Python

Google's Gemini API powers everything from simple text generation to multimodal applications that process images, audio, and documents.

AI Testing

How to Test the Claude API: A Complete Guide for Python Developers

Testing AI APIs requires a different mindset than testing traditional REST endpoints.

AI Testing

LLM Safety Testing Checklist: Jailbreaks, Prompt Injection, and Content Policy Violations

Deploying an LLM-powered application without safety testing is shipping code without security review.

AI Testing

LLM Output Schema Validation: Enforcing Structured JSON from AI Models

The most common source of production failures in LLM applications isn't a bad answer — it's a bad format.

AI Testing

LLM Evaluation Metrics: How to Measure AI Model Quality

Measuring the quality of a large language model output is fundamentally different from measuring traditional software behavior.

AI Testing

LLM Benchmarks Explained: What MMLU, HumanEval, and HELM Actually Measure

When a model release announces "state-of-the-art on MMLU" or "beats GPT-4 on HumanEval," how much should you care?

AI Testing

A/B Testing LLM Models and Prompts: A Statistical Framework

"The new prompt feels better" is not an evaluation strategy. Moving from GPT-4 to Claude, or changing a system prompt, requires rigorous A/B testing to make confident decisions — especially when the differences in quality are subtle and user impact is significant. This guide covers the statistical

AI Testing

Continuous LLM Evaluation: Building an Evals Pipeline for Production AI

Deploying an LLM is not a one-time event. Prompts change. Models get updated. Retrieval indexes get refreshed.