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200: Evaluating LLM Systems

Topic Overview

Building an AI application is one thing; proving that it works correctly, reliably, and safely is another. Evaluation is the systematic process of measuring the performance of an AI system. In the world of AI Engineering, this is significantly more complex than traditional machine learning.

This Map of Content (MOC) provides a structured path to understanding the challenges and techniques for evaluating modern AI systems.

The Core Challenge: Why is LLM Evaluation So Hard?

Evaluating LLMs is difficult because their tasks are often complex, open-ended, and subjective. Unlike a simple classification problem with a single right answer, an LLM's output can be valid in countless ways.

flowchart TD
    A[Traditional ML Evaluation<br/>e.g., Image Classifier] --> B{Is prediction 'cat'<br/>when label is 'cat'?}
    B -->|Yes| C[✅ Correct]
    B -->|No| D[❌ Incorrect]

    subgraph clear [Clear, Objective Metrics]
        C
        D
    end

    E[LLM Evaluation<br/>e.g., Summarize this article] --> F{Is the summary good?}
    F --> G{Is it factually correct?}
    F --> H{Is it coherent?}
    F --> I{Does it capture the main points?}
    F --> J{Is the tone appropriate?}

    subgraph complex [Complex, Subjective Metrics]
        G
        H
        I
        J
    end

    style A fill:#e3f2fd,stroke:#1976d2
    style E fill:#fce4ec,stroke:#c2185b

Evaluation Approaches

Intrinsic Metrics

Metrics that measure the model's inherent capabilities without reference to downstream tasks: - Perplexity: Measures how well the model predicts text - Token-level accuracy: Precision of individual token predictions - Fluency scores: Assess language quality and coherence

Extrinsic Metrics

Metrics that evaluate performance on specific tasks or real-world outcomes: - Functional Correctness: Did the system accomplish its intended goal? - Task-specific benchmarks: Performance on standardized evaluation datasets - Human evaluation: Expert assessment of model outputs

Key Principles

1. Alignment with Business Objectives Evaluation metrics should directly relate to the value the AI system provides to users and stakeholders.

2. Multi-dimensional Assessment No single metric captures all aspects of LLM performance. A comprehensive evaluation framework requires multiple complementary metrics.

3. Iterative Refinement Evaluation approaches should evolve as understanding of the system's capabilities and limitations deepens.

Course Navigation

This course material is part of the AI Engineering interactive course for beginners.