Insights and recommendations

The request is decomposed into the data needed, that data is retrieved from authorised sources, and the model compares and explains. The result cites the records it used and names the period it examined.

Comparison questions work best when you fix the comparison yourself rather than letting OpenFeather choose:

"Compare revenue by channel this quarter versus last quarter, and name the biggest movers"
"What drove the change in support volume between week 1 and week 4?"

• Each recommendation should be traceable to at least one cited finding.
• Recommendations can be wrong when the underlying data is partial.
• Never treat a recommendation as an executed plan; prepare it, review it, then act deliberately.

1. 1Check the period matches the period you care about.
2. 2Open at least one cited record and confirm the number.
3. 3Look for a plausible alternative explanation — seasonality, a data gap, a pricing change.
4. 4Re-run the question with a narrower filter to see whether the pattern survives.

• Not a measurement — it is a pattern inferred from measurements.
• Not a forecast — no model here guarantees a future outcome.
• Not a diagnosis — it describes what the data shows, not why it happened.

When you need certainty about a single figure, ask for the figure and its source rather than for an insight about it. Insights are for direction; records are for facts.

Recording the period and the population behind an insight is what makes it reusable. An insight with no stated scope cannot be compared with next month's insight, because the two may not describe the same thing.