Verification
Checking what AI gives you against something real, such as the source, the data, an expert or your own knowledge, before you rely on it or pass it on.
Also called: fact-checking · checking AI output · double-checking · source checking · discernment
Connections
Understand first
Ideas this one builds on.
Leads on to
Ideas that build on this one.
Related
Often used together or easily confused.
Skills that use it
Blueprints that practise it
- Ask AI to find where your draft misleads (garden)
- Compare AI tools on a task you actually do (garden)
- Write a handover brief before you give an agent a real task (garden)
- Turn messy notes into a status draft you can check (garden)
- Write a one-page build plan before you ask AI to make an app (garden)
Resources that teach it
- AI Capabilities and Limitations (garden)
- AI Fluency: Framework & Foundations (garden)
- The AI Fluency Index (garden)
- AI Fluency for Nonprofits (garden)
- How to Turn a One-Off AI Chat Session into a Reusable Workflow for Your Team (garden)
- The Impact of Generative AI on Critical Thinking (garden)
- Beyond vibe checks: A PM's complete guide to evals (garden)
- The non-technical PM's guide to building with Cursor (Lenny's Podcast, with Zevi Arnovitz) (garden)
- Vivienne Ming on hybrid collective intelligence, building cyborgs, meta-uncertainty, and the unknown infinite (garden)
- The AI That Asks Better Questions: Dr. Vivienne Ming at SIM 2025 (garden)
- Centaurs and Cyborgs on the Jagged Frontier (garden)
- Management as AI superpower (garden)
- On Working with Wizards (garden)
- 12 Rules to Vibe Code Without Frustration (garden)
- Build Beautiful Slides with Claude Code in 12 Minutes (12 Formats + 3 Templates) (garden)
- How to be more ambitious: A tutorial for humans with models (garden)
- Evaluating AI for high-stakes work (garden)
Competency
Find verification on the commons map ↗
You may know it from
- journalism fact-checking Every name, number and quote is checked against a primary source before publication.
- finance the four-eyes principle Important transactions need a second person to confirm before they go through.
In products
The same idea, under different names in different tools.
| Provider | What they call it |
|---|---|
| Perplexity | Cited answers |
| Gemini double-check response | |
| OpenAI | ChatGPT search with citations |
In plain words
Verification is the habit of checking AI output before trusting it. It sounds obvious, yet in one large study of real conversations, people checked facts in fewer than one in ten. Polished outputs got checked even less.
You don’t need to check everything equally. Match the effort to the stakes and to the kind of claim. Specific facts (numbers, names, dates, quotes, citations) need checking against a source. Reasoning needs a quick “does this follow?” Summaries of your own documents need a spot check against the original. Anything going to a funder, a client or the public deserves more care than a private brainstorm.
Always check against something outside the AI. Asking the same AI “are you sure?” is weak, because it may simply agree or change its answer to please you.
Example
A finance assistant uses AI to summarise a 30-page audit. Before sending the summary to the board, she picks the three figures that matter most and finds each one in the original PDF. One was rounded in a misleading way; she fixes it.
How it connects
- The answer to hallucination, and a guard against sycophancy.
- The jagged frontier tells you where checking matters most.
- Done systematically and repeatedly, it becomes evals.
Try it
Take one AI answer you used this week and, without rereading it, write down the claim that would hurt most if it were wrong. Check that claim against a real source.
Read this card as plain Markdown: /concepts/con-verification.md. Know a parallel from your own field, or a product that implements this? Add it.