Module 25: Evaluation, Experiment Design & Observability

Chapter Overview

Building LLM applications is only half the challenge; knowing whether they actually work is the other half. Unlike traditional software where correctness is binary, LLM outputs are probabilistic, subjective, and context-dependent. A model that performs brilliantly on one prompt may fail catastrophically on a slight rephrasing. This fundamental uncertainty makes rigorous evaluation, principled experiment design, and continuous observability essential for every LLM project.

This module covers the complete evaluation and monitoring lifecycle. It begins with core evaluation metrics (perplexity, BLEU, ROUGE, BERTScore, LLM-as-Judge) and standard benchmarks (MMLU, HumanEval, MT-Bench, Chatbot Arena). It then addresses experimental design with statistical rigor, including bootstrap confidence intervals, paired tests, and ablation studies. Specialized evaluation for RAG and agent systems follows, covering RAGAS metrics, trajectory evaluation, and frameworks like DeepEval and Phoenix.

The module also covers testing strategies for LLM applications (unit tests, red teaming, prompt injection testing, CI/CD integration), production observability with tracing tools (LangSmith, Langfuse, Phoenix), monitoring for prompt drift, provider version drift, and embedding drift, and reproducibility practices including prompt versioning, config management with Hydra, experiment tracking with MLflow and Weights & Biases, and containerized reproducibility with Docker.

Learning Objectives

Select and compute appropriate evaluation metrics for different LLM tasks (generation, retrieval, reasoning, code)
Design statistically rigorous experiments with bootstrap confidence intervals, paired tests, and proper ablation controls
Evaluate RAG pipelines using RAGAS metrics and agent systems using trajectory-based evaluation
Build automated test suites for LLM applications including unit tests, integration tests, and red-team adversarial tests
Instrument LLM applications with distributed tracing using LangSmith, Langfuse, or Phoenix
Detect and respond to prompt drift, model version drift, and embedding drift in production systems
Implement reproducibility practices including prompt versioning, config management, and experiment tracking
Integrate evaluation into CI/CD pipelines using assertion-based testing and promptfoo
Design quality monitoring dashboards with alerting for production LLM applications

Prerequisites

Module 09: LLM APIs (chat completions, message formatting, model parameters)
Module 10: Prompt Engineering (prompt design, structured outputs, chain-of-thought)
Module 19: Retrieval-Augmented Generation (embedding search, vector stores, RAG pipelines)
Module 21: AI Agents (agent architectures, tool use, planning patterns)
Familiarity with Python testing frameworks (pytest) and basic statistics

Chapter Overview

Learning Objectives

Prerequisites

Sections