AI in practice.
Practical AI workflows, capability limits, privacy trade-offs and honest tool comparisons.
An AI workflow you can verify
Use AI for a clearly bounded draft or analysis task, then treat the output as unverified until you check it. A confident answer is not a source, and a fluent summary is not evidence of testing.
No invented benchmarks, automation claims or personal tests. We distinguish vendor claims from independent evidence.
Start with a clear decision.
This category is a practical orientation. Model-specific capability and pricing articles will require current provider documentation and clearly dated checks.
Browse existing practical guides ↗A useful starting method.
Concrete steps you can apply now. Examples are illustrative, not claimed tests or live product prices.
Give the model a defined task
Start with a prompt that contains goal, audience, supplied facts, constraints and output format. For example: “Using only these three product descriptions that I wrote, make a comparison table of dimensions and materials. Leave missing values blank. Do not guess.” This makes omissions visible and easier to check.
Separate your own notes from facts copied from other sources. Do not paste passwords, government IDs, sensitive customer data or unpublished confidential material into an external AI service without the necessary authorization.
Build a human verification loop
Check every consequential number, link, quote, price, release date and technical instruction against the original source. Follow links yourself; a model can produce convincing but incorrect references. Ask whether a claim describes a vendor announcement, a third-party test or your own observed result—these are different levels of evidence.
For high-stakes issues involving health, law, money or safety, use qualified primary guidance and appropriate expert review. Do not publish AI output as confirmed reporting merely because several generated answers agree.
Keep a before-and-after record
Save the original task, an example input that is safe to share, the AI-assisted draft, the corrections made and the final review result. Measure usefulness by a real outcome: fewer manual formatting steps, clearer instructions or a verified comparison—not an invented productivity percentage.
The four-point check.
Use this short list as a quality gate for your own work. A missing answer is a reason to pause and verify.
- The task and source boundaries are explicit.
- Sensitive information was excluded.
- Material claims were checked outside the model.
- The final output was reviewed and corrected by a person.