Evaluation & Testing LLM Applications
You would never deploy a web app without tests. You would never ship a database migration without a rollback plan. But right now, most teams ship LLM applications by reading 10 outputs and saying "yeah, looks good." That is not evaluation. That is hope. Hope is not an engineering practice. Every prompt change, every model swap, every temperature tweak changes your output distribution in ways you cannot predict by reading a handful of examples. Evaluation is the only thing standing between your application and silent degradation. Related: Phase 5 · 27 (LLM Evaluation — RAGAS, DeepEval, G-Eval) covers the framework-level concepts (NLI-based faithfulness, judge calibration, the RAG four). Phase 5 · 28 (Long-Context Evaluation) covers NIAH / RULER / LongBench / MRCR for context-length regression. This lesson focuses on what is LLM-engineering-specific: CI/CD integration, cost-gated eval runs, regression dashboards. Build an evaluation dataset with input-output pairs, rubrics, and edge cases specific to your LLM application. Implement automated scoring using LLM-as-judge, regex matching, and deterministic assertion checks. Set up regression testing that detects quality degradation when prompts, models, or parameters change. Design evaluation metrics that capture what matters for your use case (correctness, tone, format compliance, latency). You build a RAG chatbot for customer support. It works great in your demos. You ship it. Two weeks later, someone changes the system…
Evaluation & Testing LLM Applications: You would never deploy a web app without tests. You would never ship a database migration without a rollback plan. But…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
Browse the complete course catalog or open this lesson on GitHub.