AI Engineering/Phase 11/Lesson 10

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.

Build 45 min Python

Prerequisites: Phase 11 Lesson 01 (Prompt Engineering), Lesson 09 (Function Calling)

#Learning Objectives

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)

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.