Software Tests vs AI Evals: Why AI Applications Need a Different Way of Testing

2026-07-10 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

Traditional software testing, built on the principle of deterministic outputs and verifiable correctness, is inadequate for modern AI applications, especially those powered by Large Language Models (LLMs) that generate natural language. Unlike traditional software, LLMs can produce multiple valid responses for the same input, making "exact equality" comparisons irrelevant. AI Evals (Evaluations) address this by shifting the focus from verifying "correctness" to measuring "quality." Evals systematically assess an AI application's behavior against its intended goal, considering multiple dimensions such as correctness, relevance, helpfulness, safety, latency, and cost. This approach allows engineering teams to confidently iterate on prompts, models, and retrieval strategies, transforming AI development from trial-and-error into a measurable engineering discipline.

Key takeaway

For AI Engineers building production AI systems, relying solely on traditional software tests for generative AI applications is insufficient. You must adopt AI Evals to systematically measure response quality, rather than just verifying correctness. Implement evaluation datasets and metrics like relevance, helpfulness, and safety to confidently iterate on prompts, models, and RAG strategies, ensuring reliable system improvements.

Key insights

AI Evals shift testing from verifying deterministic correctness to evaluating non-deterministic quality in generative AI.

Principles

• Traditional tests verify deterministic correctness.
• AI Evals measure non-deterministic response quality.
• Multiple valid outputs challenge traditional testing.

In practice

• Apply Evals to generative AI systems.
• Assess AI outputs across multiple quality metrics.
• Build evaluation datasets for confident iteration.

Topics

• AI Evals
• Large Language Models
• Software Testing
• Generative AI
• Quality Metrics
• AI Engineering

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

Related on AIssential

• LLM Evals: Basics — LLM evaluation is crucial because AI systems are probabilistic, unlike deterministic traditional software.
• Stop Asking Which AI Writes Better Code. Start Making Them Compete. — AI code generation models should compete in automated pipelines, proving their implementations with evidence, not benchmarks.
• From Exams to Escape Rooms: How We Learned to Test AI — AI evaluation is a dynamic process, constantly adapting to measure increasingly complex model capabilities beyond narrow task performance.
• Evals Skills for Coding Agents — Improving infrastructure around AI agents, especially evaluation capabilities, is more critical than solely improving the underlying model.
• Evaluation in Production GenAI: Why Quality Is a System Design Problem — GenAI evaluation requires a multi-layered, continuous feedback loop to bridge the gap between test and production quality.
• LLM Vs AI: A Practical Guide to Differences, Use Cases, and Tools — Generative AI and LLMs are transforming enterprise operations through diverse applications, requiring careful selection and deployment.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.