Truth and current knowledge

Generated language does not inherently verify truth or know the present. Search and retrieval can help, but source quality and interpretation still matter.

Use this three-part method:

1. Separate stable explanation from current factual lookup. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
2. Require primary sources for time-sensitive claims. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
3. Verify citations and dates before acting. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.

Walk through A model explains a stable concept well but invents this morning’s policy update because it has no live source.. Label three moments where “Truth and current knowledge” changes what you trust: (1) the first fluent answer, (2) the first missing source or permission, and (3) the decision a human must own. Write the before/after task so the model only does the slice that evidence supports. Keep one sentence that states how this page’s idea differs from a generic “AI is smart/dumb” score.

• Assuming training data is a complete encyclopedia. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
• Believing browsing guarantees correct synthesis. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
• Accepting an answer because it includes links. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.

Before you close the tab, capture a reusable habit for Truth and current knowledge inside What AI can and can't do: name the observable check, the evidence you would open, and the stop condition. Rehearse it once on a low-stakes example, then once on a higher-stakes variant. The habit succeeds when you can explain the check without reopening this lesson. Target outcome: Explain AI capabilities as task-specific and conditional rather than magical.

Return to the can and cant scenario and restate what this chapter proved on page truth and current knowledge.mdx. Name one metric, one ownership rule, and one regression test you will keep. Explain how this page connects to the previous page without repeating earlier paragraphs. If you cannot name a falsifier, the chapter is still a story rather than a controlled practice. Write the falsifier as an observable event with a threshold.

Return to the can and cant scenario and restate what this chapter proved on page truth and current knowledge.mdx. Name one metric, one ownership rule, and one regression test you will keep. Explain how this page connects to the previous page without repeating earlier paragraphs. If you cannot name a falsifier, the chapter is still a story rather than a controlled practice. Write the falsifier as an observable event with a threshold.