Most of the value in Caching Behavior appears before anyone says done. The useful work is usually in the questions, the examples, and the evidence that changes the conversation.
When I review work in Caching Behavior, I am not only asking whether the ticket appears complete. I am asking whether the evidence, code behavior, and surrounding assumptions fit together tightly enough that I would trust the result after release. The risk never stays theoretical for long, because the feature was fixed, but users still see yesterday's truth because cache invalidation fell behind.
The review becomes useful when it tests the story behind the result, not just the result itself.
The First Signals I Look For
- Does the implementation clearly support freshness, invalidation, and the strange bugs caused by old state surviving too long?
- Is the risky path visible, or has it been left to assumption?
- Would another reviewer understand the user impact without extra verbal explanation?
Questions I Ask Before I Call It Ready
I ask what changed outside the happy path, what happens under interruption, and how the team would know it failed in real use. With Caching Behavior, those questions matter because a profile update succeeds yet another screen keeps showing the old value for minutes.
I also want to know whether the work can be explained to teams debugging inconsistency across screens or services without hand-waving. If the answer needs too much translation, there is often still a hidden gap.
What Good Evidence Looks Like to Me
Good evidence is easy to point to and hard to misunderstand. For this topic I am looking for something like cache keys, expiry rules, and proof that important state refreshes when users expect it to.
I hold the review when the result depends on a promise nobody verified, when a negative path was skipped because it seemed unlikely, or when the notes only show activity instead of meaning. I keep the practice alive because it improves both release quality and team clarity at the same time.