Testing AI

How do you test software when the same input does not always produce the same output?

Traditional software testing depends on predictable behavior: provide an input, compare the result with an expected output, and determine whether the system passes or fails. AI changes that equation.

Large language models can generate multiple valid responses. AI agents may take different paths toward the same objective. Retrieval systems depend on changing context, and model or prompt updates can improve one capability while quietly degrading another. An AI application that performs brilliantly in a demonstration can still fail when confronted with real users, edge cases, unexpected inputs, and production workloads.

Testing AI: Engineering Confidence in Non-Deterministic Systems is a practical guide to tackling this new generation of quality-engineering challenges.

Designed for software engineers, QA professionals, AI/ML engineers, developers, technical leaders, and teams building AI-powered products, this book shows how to move beyond traditional pass/fail testing and develop systematic methods for evaluating the quality, reliability, and performance of non-deterministic systems.

• Design meaningful evaluations for LLM-powered applications
• Define and measure quality when outputs are probabilistic
• Build repeatable automated evaluation pipelines
• Create representative datasets and effective test cases
• Combine deterministic checks, human evaluation, and model-based evaluation
• Test prompts, structured outputs, tool use, and multi-step workflows
• Evaluate retrieval-augmented generation and grounded responses
• Test AI agents for task completion and tool interactions
• Detect hallucinations, regressions, inconsistencies, and unexpected behavior
• Establish useful metrics, baselines, thresholds, and acceptance criteria
• Integrate AI evaluations into development and CI/CD workflows
• Use observability to understand production behavior
• Monitor quality degradation and emerging failure patterns
• Turn production feedback into stronger evaluation suites

AI quality cannot always be reduced to one correct answer. Effective testing must account for variation, context, factuality, relevance, robustness, task completion, latency, cost, and the consequences of failure.

This guide helps you build an engineering approach to that uncertainty. Instead of treating evaluation as a final checkpoint, you'll learn how to incorporate testing throughout the AI development lifecycle-from early experimentation and prompt changes to regression testing, deployment, monitoring, and continuous improvement.

A compelling prototype is only the beginning. Production AI must perform across unpredictable interactions while models, prompts, retrieval pipelines, tools, data, and user behavior continue to evolve.

Whether you're building an LLM application, RAG pipeline, AI assistant, agentic workflow, or generative AI product, this book provides practical strategies for turning uncertain behavior into measurable engineering evidence.

Stop relying on impressive demos and intuition alone. Build evaluation, testing, and observability practices that help reveal when your AI works, where it fails, and whether it is ready for production.

Get your copy of Testing AI today and start engineering confidence into every stage of your AI development lifecycle.