How to Check AI Summaries and Drafts Before You Use Them

AI output can be wrong or off-brand. A few quick checks reduce the risk of spreading errors or tone that doesn't fit.

Why validation matters

Summarization tools can omit important facts, flip meaning, or invent details. Writing tools can sound generic, add unsupported claims, or drift from your voice. Checking before you publish or send protects your credibility and avoids rework.

What to check by output type

Summaries

• Compare two or three main points to the source. Are they correct?
• Are numbers, names, and dates unchanged? If not, correct them or regenerate.
• Is the emphasis right? If the source is critical of something, the summary should not present it as positive.

Drafts (emails, posts, copy)

• Read once for tone. Does it match your brand and audience?
• Spot-check any factual claims against your notes or the source. Remove or fix anything that's wrong.
• Check length and structure. Shorten or expand as needed.

Data or structured output

• If the tool returns numbers or dates, compare a sample to the original data or API.
• If it generates links, open a few to confirm they're correct and not broken.

A simple process

1. Generate the output.
2. Compare to the source (or your brief) and fix errors.
3. Edit only what's wrong; keep the rest.
4. If you changed the input and care about consistency, run the tool once more and check again.

Over time you'll learn which tools and tasks need more or less checking. For high-stakes or regulated content, keep a human in the loop every time; for internal or low-stakes use, a quick scan may be enough.