LAB CATEGORY
Explore classification, prompts, structured output, and model metadata as parts of one reviewable workflow. The focus is practical: define the task, preserve evidence, validate the response, and evaluate errors on representative documents. Use these articles together to understand where a model can assist and where a clearer contract or editorial decision is needed.
4 ARTICLES IN THIS COLLECTION
Begin with the evaluation article and define what a correct label would allow your application to do. Choose difficult documents before choosing the wording of a prompt. Next, read the prompt and JSON extraction guides together: one describes the decision rule, while the other describes the checks that make its output usable. Ask whether every uncertain result has a clear next step.
Use the model taxonomy article when your archive also discusses AI systems themselves. Which claims belong to a named model version, and which depend on retrieval, preprocessing, or an application configuration? Keep that distinction in your review notes. A practical reading outcome is an evaluation sheet linking each observed error to the component that could fix it: vocabulary, prompt, source preparation, validation, or editorial review.
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
Valid JSON is the first check in a longer workflow. Design topic extraction around an allowed vocabulary, verifiable evidence, explicit outcomes, document revisions, and validation that connects a generated label to its source.
Read the article : LLM topic extraction: a JSON contract you can actually validate
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 labelsFollow the core topic guides for examples, field definitions, and related reading.