Module 7: Evaluation for NLP systems

AINS6004 — Natural Language Processing

Essential Question

Why are output quality and factuality hard to measure?

Scenario

a product team evaluating an NLP workflow before using it in customer-facing communication

Stakeholders: product manager, support lead, privacy reviewer, and model evaluator

Core Moves

• Define the decision boundary

• Compare baseline and alternative

• Interpret evidence and assumptions

• Identify failure modes

• Recommend next action

Lab & Assignment

Create an evaluation set with rubrics and automated checks.

Artifact: NLP evaluation packet with task framing, retrieval/evaluation design, and deployment guardrails focused on evaluation for nlp systems: Create an evaluation set with rubrics and automated checks.