Decide where the work belongs
Name the search consequence
Describe whether people fail to find a product, refine successfully, or leave after irrelevant results. Compare zero-result, repeated reformulation, and useful filter paths. Keep behavior tied to a known query family and session boundary.
Compare evidence quality
Weigh observed result state, selection, return behavior, sampling, and missing events separately. Consider whether the signal supports copy, facet, vocabulary, or ranking work. Do not turn a sparse learning trace into an unsupported demand estimate.
Choose a queue move
Set owner, query scope, next experiment, and revisit signal. Decide whether to collect more paths, test a bounded change, or document a known pattern. Preserve the original session examples so later evidence can challenge the interpretation.
What to carry forward
Prioritize query learning work where repeated behavior points to a meaningful search barrier and the next check can be bounded. The decision should state sample limits and remedy scope. If intent remains ambiguous, prioritize evidence collection over a global search rule.
Technical background: Elastic documentation.
Keep the decision with the work.
Use a Work Item in Aglet to record the problem, the evidence you have, and the next decision. Add an owner and priority, then keep updates in the discussion so the next person can follow the reasoning.
Create an account See the product workflow