LAB TOPIC / EVALUATION
Evaluation should show where assignments help and where they mislead. Read about representative examples, category-specific errors, review policies, and the conditions behind reported results. Keep the test set and the intended user task visible whenever you compare approaches.
4 CONNECTED ARTICLES
Start by naming an error that would materially change the reader's experience. An unsupported primary label can misplace an article; a false duplicate merge can hide a correction; an incorrect entity link can contaminate a company archive. Use that error to select representative cases and review criteria. Ask what evidence would convince you that a revision improved the intended task.
As you read, separate the reference decision from the model's suggestion and the reported percentage from its denominator. Can another reviewer reconstruct why an example was labeled? Are repeated versions of one story dominating the sample? Keep a stable comparison set for regression review, and reserve unfamiliar material for later evaluation. The practical deliverable is a release note that names improved cases, remaining failures, and the limits of the tested collection.
A strong labeling prompt describes a decision that another editor could follow. Learn to separate instructions from source text, choose examples that reveal topic boundaries, handle uncertainty, and evaluate changes against a stable review set.
Read the article : Prompt design for topic labels: definitions, evidence, and edge cases
Repeated posts can make one story look like many independent signals. Learn how to separate activities, content objects, and story clusters, then report observed topic patterns with clear sampling limits and evidence.
Read the article : Separate Social Topic Signals from Repeated Posts
Organize model coverage by task, modality, and evidence. This practical taxonomy separates the model being discussed from the system using it, while keeping broad capability claims attached to their sources and evaluation scope.
Read the article : Build an AI Model Taxonomy That Keeps Claims in Context
A useful evaluation starts with the decisions a label will drive. Build a representative review set, calculate topic-level metrics, inspect disagreements, and compare revisions without hiding costly mistakes in a single average.
Read the article : Evaluate AI topic classification before you trust the labels