How to Build a Golden Dataset for Repeatable AI Evaluations
A golden dataset is a curated, versioned set of inputs with reviewed expected outputs or explicit pass criteria, used as the fixed reference you run before every…
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Testing & Responsible Use
Methods for testing AI outputs, protecting privacy, and documenting responsible use.
24 published articlesHow to Evaluate RAG Retrieval Quality with a Small Test Set
Build a small, versioned set of real questions paired with the chunk IDs that should answer them, run only the retriever against it, and gate recall@k and…
How to Test AI Integrations: Mocking, Golden Datasets, and Regression Tests
LLM integrations cannot be tested with exact string assertions, because the same input can produce different wording on every call. The workable approach is layered: mock the…
Designing an AI Content Review Rubric with Quality Scores and Rejection Reasons
A review rubric converts subjective approval into two machine-readable outputs: a score per quality axis and a named rejection reason when a draft fails. This guide explains…
How to Evaluate and Reduce Hallucinations in LLM Workflows
Hallucinations are best handled as two linked tasks: measuring them with a labelled evaluation set, and reducing them with grounded retrieval plus an output guardrail. This guide…
How to Secure a Local AI Endpoint for WordPress with HTTPS and API Keys
A local AI runtime such as Ollama, LM Studio, or llama.cpp usually ships with no authentication and a plain-HTTP listener bound to localhost. WordPress should never talk…
How to Evaluate Hallucinations in a Local Model with Your Own Test Set
A hallucination evaluation for a local model needs three things: questions whose answers you have verified, a cold run with no tools or retrieval, and a grading…
How to Create Regression Tests for AI-Generated WordPress Content
AI-generated WordPress content cannot be regression-tested with exact-match assertions, because correct output varies between runs. The practical replacement is an eval: a scored check that runs a…
How to Benchmark Local LLMs for Code Generation with a Fixed Test Suite
A fixed test suite replaces impressions with a comparable number: freeze the tasks, the harness, the runtime, and the scoring rule, then run the same suite repeatedly.…
How to Add Human Review Before AI Content Is Published
Adding human review before publication means inserting an explicit approval state between generation and publishing, binding that approval to a specific version of the content, and letting…
How to Check Whether an AI Citation Actually Supports a Claim
An AI citation supports a claim only when the cited source, read in context, actually states that claim. This guide explains how to resolve citations, compare claims…
How Context Length Affects Local AI Memory Usage
Raising a local model's context window from 2K to 32K changed generation speed by essentially nothing in a published llama.cpp sweep, but added about 1.7 GB of…