LLM Evaluation 101: Why You Can't Test an LLM Like You Test Your Code

2026-06-20 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Novice, short

Summary

This article introduces the fundamental difference between evaluating Large Language Models (LLMs) and testing traditional software. Unlike deterministic software, which yields identical outputs for the same input, LLMs produce varied responses to identical prompts, making binary pass/fail string matching ineffective. Consequently, LLM evaluation requires assessing a "whole set of dimensions" rather than a single correctness check. Key evaluation dimensions for LLM applications, such as RAG-based chatbots, include factuality, completeness, tonality, groundedness, latency, and cost. The specific dimensions prioritized depend entirely on the application's use case; for instance, a customer support bot values tonality and groundedness, while a code-generation assistant prioritizes correctness and executability. This foundational understanding is crucial for navigating the complexities of LLM evaluation, which will be explored further in an upcoming series covering benchmarks, evaluation pipelines, and specific application types like RAG and agent-based systems.

Key takeaway

For MLOps Engineers building LLM-powered features, abandon traditional deterministic software testing paradigms. Your evaluation strategy must shift from binary pass/fail checks to a multi-dimensional assessment, considering factors like factuality, tonality, and groundedness. You need to define "good" specifically for each LLM application, as relevant evaluation dimensions vary significantly by use case. This tailored approach ensures you accurately measure performance and deliver reliable, contextually appropriate LLM outputs, preventing misaligned development efforts.

Key insights

LLM evaluation is non-deterministic and requires multi-dimensional assessment tailored to specific application needs.

Principles

• LLMs are non-deterministic; same input yields different outputs.
• Evaluation must assess multiple dimensions, not binary correctness.
• "Good" is application-specific, requiring tailored frameworks.

Method

Evaluate LLM applications across dimensions like factuality, completeness, tonality, groundedness, latency, and cost, customizing the framework for each use case.

In practice

• Define "good" for your specific LLM application.
• Prioritize evaluation dimensions based on use case.
• Move beyond pass/fail string matching for LLM outputs.

Topics

• LLM Evaluation
• Software Testing
• Non-deterministic Systems
• RAG Systems
• Evaluation Metrics
• Application-Specific Evaluation

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.