Systems, agents, retrieval, infrastructure

AI Observability, Evals, and Regression Testing

Traditional monitoring tells you whether a service is up; AI observability tells you whether a generated answer was useful, grounded, safe, and cheap enough. This module connects traces, prompts, retrieved evidence, tool calls, latency, token usage, and user feedback into one debuggable system.

Deep dive summary

What this module actually teaches.

Passage 1Traditional monitoring tells you whether a service is up; AI observability tells you whether a generated answer was useful, grounded, safe, and cheap enough. This module connects traces, prompts, retrieved evidence, tool calls, latency, token usage, and user feedback into one debuggable system.

Passage 2Learners build an evaluation mindset around golden datasets, adversarial tests, pairwise model comparisons, rubric-based judging, retrieval metrics, human review queues, and release gates. The curriculum stresses that evals must be versioned with prompts, tools, models, and product requirements.

Passage 3The final section covers regression workflows for AI changes: shadow traffic, canaries, prompt diffs, rollback criteria, and incident analysis. Students leave with a practical system for improving AI quality without trusting vibes or isolated demo prompts.

Learning operating system

Module prerequisites, concepts, outcomes, and artifacts

Prerequisites

  • Logging and tracing basics
  • test design
  • familiarity with RAG or tool calling

Key concepts

  • LLM tracing
  • golden datasets
  • rubric judges
  • retrieval metrics
  • prompt versioning
  • shadow traffic
  • AI incident reviews

Target audience

  • Engineering leads, AI platform teams, QA automation engineers, and product engineers responsible for quality gates.

Outcomes

  • Define eval suites aligned to product requirements
  • Trace failures across retrieval, prompting, model choice, and tools
  • Create release gates for prompt and model changes

Artifacts

  • AI eval suite template
  • trace inspection checklist
  • read-only judge rubric example

Code example

Real patterns, ready to copy into your editor.

A code reference card that frames this module's concepts in real syntax. Copy any example to study it or adapt it directly in your own editor.

Copy-ready snippets
curriculum/se-ai-observability-evals-regression.tsts
1type ModuleBrief = {2  track: string;3  difficulty: string;4  keyConcepts: string[];5  reviewGate: 'human-review-required';6};7 8export const moduleBrief: ModuleBrief = {9  track: 'Software Engineers',10  difficulty: 'Masterclass',11  keyConcepts: [12    'LLM tracing',13    'golden datasets',14    'rubric judges',15    'retrieval metrics',16    'prompt versioning',17  ],18  reviewGate: 'human-review-required',19};