Tagged
#reliability
9 articles
Test your prompts like code
A prompt is code that ships to users. Treat it that way — with test cases, a baseline, and a regression check before every change.
Add citations to AI answers
Citations turn an unverifiable answer into a checkable one. Here is how to get a model to cite its sources, and to cite them honestly.
Reduce hallucinations: a practical checklist
Models invent facts when the task invites them to. This checklist covers the moves that cut hallucinations without pretending you can eliminate them.
Hallucination, explained without the panic
A language model that makes things up is not malfunctioning — it is doing exactly what it was built to do. Here is why hallucination happens and how to manage it.
Handle errors and timeouts gracefully
Model calls fail, stall, and rate-limit. A practical guide to retries, timeouts, fallbacks, and fail-safe behavior that keeps an AI feature reliable.
Write a system prompt that works
A system prompt sets the rules before the conversation starts. Here is how to write one that holds up across real inputs, not just demos.
AI agents at work: realistic tasks vs demo theater
Agent demos are dazzling and agent deployments are humbling. Here is what actually works at work, what falls apart, and how to tell which is which.
Rate limits and retries: building resilient LLM calls
Hosted LLMs fail in ordinary ways — limits, timeouts, transient errors. A little retry discipline turns a fragile integration into a dependable one.
Why two runs of the same prompt differ
"Send the same prompt twice and you often get two different answers. That is by design, not a bug — and knowing why tells you when to control it."