Documentation explaining how things work, measurement results, or lessons from failures must use plain language. The documentation should include examples. This helps prevent misunderstandings and saves time.
By Pawan Nayar · from the LLOS AI Learning record
Documentation explaining how things work, measurement results, or lessons from failures must use plain language.
The documentation should include examples. This helps prevent misunderstandings and saves time.
Documentation files that explain why things work need plain language. They should include examples. This makes the information easy to understand for everyone. Using short, coded language can cause confusion. People often read these files when something is broken. Clear explanations help fix problems faster. Short lists are good for simple facts like names or costs.
A developer writes a file explaining a new software feature. The developer uses technical jargon and short bullet points. Another developer later reads the file to fix a bug. The second developer cannot understand the explanation quickly. This causes a delay in fixing the bug.
Resolved from this lesson's own record — real definitions, not invented for this page.
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When an AI model asks a user to test, open, refresh, check, or verify something, it must include the full web address or file path. Do not use phrases like 'refresh the page' or 'open the file'.
Technical words and jargon make lessons hard to understand. A person reading the lesson for the first time will not understand it.
An AI model should use plain English. It must not use special AI words or jargon.
An AI model should use short sentences. The AI model should choose simple words.
Documentation explaining how things work, measurement results, or lessons from failures must use plain language. The documentation should include examples. This helps prevent misunderstandings and saves time.