Eval-Driven AI: Evaluation, Model Selection, Testing & Guardrails

Build a proper framework around LLM evaluation: why evals are the regression tests for AI systems, how to select models on evidence instead of hype, how to build golden test data, the evaluator toolbox (exact match, rubrics, LLM-as-judge, RAG metrics), the guardrails layer, and concrete implementation walks through LangSmith, LangGraph, and Microsoft Copilot Studio — ending with hard production gates and measuring change over time.

intermediate 120 min 11 lessons

Lessons

  • The Eval Mindset: Why You Can't Ship Without a Measurement Loop

    Why LLM applications break the traditional testing model, the 'evals are regression tests' mental model, the eval-driven development loop, and the core vocabulary (dataset, run, evaluator, score, gate).

    9 min
  • Model Selection: Choosing the Right Brain on Evidence

    What public benchmarks actually measure and where they lie, the quality/latency/cost triangle, the hidden axes (structured output, tool calling, injection resistance, portability), and the evaluate-before-you-commit workflow.

    9 min
  • Building Test Data: Your Golden Dataset Is Your Source Code

    The four-bucket taxonomy (happy path, edge cases, failure modes, adversarial), sourcing real vs synthetic data, reference answers, dataset hygiene, versioning, and avoiding contamination.

    10 min
  • The Evaluator Toolbox: Scoring Techniques

    The instrument hierarchy from exact match to LLM-as-judge, documented judge biases and mitigations, RAGAS pipeline metrics, and how to aggregate per-example scores into a gate.

    10 min
  • Guardrails: The Runtime Safety Layer

    The OWASP LLM threat model, guardrails by pipeline position (input, orchestration, output, action gate), the technology landscape (Azure AI Content Safety, NeMo, Guardrails AI, Llama Guard), and how guardrails and evals work together.

    10 min
  • LangSmith: Datasets, Experiments & Regression Testing

    Full implementation walkthrough: creating datasets, defining evaluators, running experiments, diffing against baselines, wiring evals into CI as a hard gate, and production online evals + annotation queues.

    11 min
  • LangGraph: Testing Your Agent, Not Just Your Prompts

    The five-level testing discipline for stateful agents: node unit tests, graph integration tests, threads/replay/time travel, human-in-the-loop approval flows, and feeding the graph into the LangSmith eval loop.

    11 min
  • Copilot Studio: Grounding, Guardrails & Analytics in a Low-Code Platform

    Generative orchestration (topics vs generative answers), knowledge grounding sources, the platform guardrail stack (content moderation, injection protection, sensitivity, escalation), evaluation tooling, analytics, and solution-based ALM.

    11 min
  • The Production Gate: Making Evals a Hard Blocker

    The layered gate stack (quality, safety, operations, security), how to set defensible thresholds from baselines, CI gates as required checks, canary rollout and kill switches, and the maturity ladder from anecdotes to drift gates.

    10 min
  • Measuring Change: From One-Off Evals to a Living Loop

    The three time scales of measurement, online evals on live traffic, feedback loops that grow the dataset, the four kinds of drift, and the ten-second dashboard that keeps quality a watched trend.

    9 min
  • Grand Quiz: Eval-Driven AI

    Twenty questions across all ten lessons — foundations, test data and scoring, guardrails, LangSmith and LangGraph, Copilot Studio, and production maintenance — with a scored grade at the end.

    10 min