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…
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Testing & Responsible Use
Methods for testing AI outputs, protecting privacy, and documenting responsible use.
15 published articlesHow 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…
Prompt Engineering: A Practical Guide to Instructions, Examples, and Evaluation
Prompt engineering is the practice of writing, testing, and revising the instructions given to a language model so its output reliably serves a defined purpose. This guide…
How to Log AI Integration Errors Without Exposing Sensitive Data
Log AI integration errors for the operator, not for the caller or the model. The workable rule is to sanitize at the adapter boundary, return a stable…
Why AI Answers Can Sound Confident and Still Be Wrong
A language model's confident tone is a property of its generated text, not evidence that the claim is true. OpenAI researchers argue that hallucinations persist because training…
How to Document a Prompt Experiment for Other Users
A prompt experiment is only reproducible if the record captures more than the prompt text. Document the immutable prompt revision, its variable and output contract, the model…
How to Write a Prompt with Clear Success Criteria
A prompt with clear success criteria states the outcome you want, the constraints that matter, the evidence the model may use, and what the final answer must…
How to Evaluate AI Answers: Accuracy, Sources, and Repeatable Testing
AI answers can be fluent, confident, and wrong. This guide explains a practical three-pillar method for checking accuracy, verifying sources with lateral reading, and running repeatable tests…
AI Privacy: Understanding Local Processing, Cloud Services, and Data Exposure
AI privacy depends on where data goes, who can access it, and what secondary uses are allowed. This guide explains local, cloud, and hybrid AI data flows,…
How to Remove Sensitive Information Before Sharing AI Logs
Sanitising a log before you paste it into an AI chat, support ticket, or issue tracker has two stages: catalogue what is actually sensitive, then either redact…
Building Reliable AI Content Workflows with Human Review
A reliable AI content workflow gives each task to the party that can do it safely, keeps every claim linked to a source, and names a human…