Aria shows its working. Every answer carries the plan it formed and the data it actually retrieved, and both are on screen without asking — which turns "do I believe this?" from a judgement call into something you can check.

[Watch the walkthrough](https://kb.trackiq.com/kb-assets/kbvideo-aria-plan-explainer-5827a036.mp4)

*Prefer to watch? A hundred and one seconds on the two parts of an answer nobody opens, and what the row count tells you on its own.*

## **The shape of an answer**

![Plan, query and rows, under one answer](/kb-assets/shot-b37-plan-queries-3bcaba.png)

Four parts, in order:

**1. Your question**, as you typed it.

**2. The plan** — an **Insights** block restating what Aria is about to do. In the example above: *"I'll get you a chart of your account's ad performance ROAS over the past eight weeks."*

**3. The data query** — a collapsible row headed **Queried your data · 56 rows**, with a green tick beside it. Expand it and you get the rows themselves.

**4. The answer** — the visual, then the written interpretation.

Parts 2 and 3 are the ones nobody opens, and they are where the value is.

## **Read the plan first**

The plan is a restatement, and restatements are where misunderstandings surface.

**Check the metric.** You asked about ROAS; the plan should say ROAS, not ACoS.

**Check the period.** "Past eight weeks" should match what you meant. If you said "recently" and the plan says eight weeks, that is Aria choosing for you — fine, but now you know.

**Check the scope.** "Your account's ad performance" is account-wide. If you meant one product, the plan has just told you it did not understand that.

**If the plan is wrong, the answer will be wrong.** Stop there and re-ask rather than reading on. It costs ten seconds instead of ten minutes. See [**Asking Aria a Good Question**](https://kb.trackiq.com/hc/knowledge-base/articles/asking-aria-a-good-question).

## **Then open the query**

The disclosure expands to a real table of what came back. In the example: **WEEK LABEL, WEEK NUM, TOTAL AD SALES, TOTAL AD SPEND, ROAS** — the ingredients of the chart above it.

This is the part that makes Aria checkable. You are not being asked to trust a summary; you can look at the numbers it was computed from.

**Spot-check one row against a dashboard.** If week of Jul 05 reads 7,930.80 in ad sales, that figure should appear on the Sponsored Ads screen for the same range and scope.

**Check the shape.** Does the table cover the whole period you asked for, or does it stop short?

**Check the granularity.** A question about weeks answered from daily rows is fine; a question about weeks answered from four monthly rows is not.

## **The row count is a signal on its own**

The header says how many rows came back, and it is worth reading before you expand anything.

| What you see | What it usually means |
|---|---|
| A plausible number for the period | normal |
| **Suspiciously small** (a handful) | the query was narrower than your question |
| **Zero, or no query at all** | Aria answered from context, not data — treat with care |
| Very large | fine, but the summary is doing a lot of compression |

**A small row count with a confident answer is the combination to watch.** Ten rows can support a sweeping statement that the data does not.

## **More than one query**

A question that needs several sources produces several query rows, each with its own count and tick. Aria joins them for you.

**That is where to look when two figures in one answer disagree** — they may have come from different queries with different scopes or periods, and the join is where the mismatch lives.

## **What the plan and query cannot tell you**

**They do not prove the interpretation is right.** Correct data can carry a wrong conclusion. The written paragraph is Aria's reading, not a fact.

**They do not show the transformation.** You see what came back and what was produced, not every step between.

**They are not the MCP call log.** That is a separate audit trail for AI tools querying your data from outside. See [**MCP Call Logs: Seeing What Your AI Tools Asked**](https://kb.trackiq.com/hc/knowledge-base/articles/mcp-call-logs).

## **A habit worth forming**

Before you act on an Aria answer, or put it in front of anyone else:

**Read the plan.** Metric, period, scope.

**Read the row count.** Does it look like the right amount of data?

**Expand the query and check one number** against a screen you trust.

Three checks, under a minute, and they catch nearly everything.

## **Related**

- [**Asking Aria a Good Question**](https://kb.trackiq.com/hc/knowledge-base/articles/asking-aria-a-good-question) — getting the plan right first time.
- [**Checking Aria's Work When an Answer Looks Wrong**](https://kb.trackiq.com/hc/knowledge-base/articles/checking-arias-work) — the full diagnostic routine.
- [**What Aria Can and Cannot Answer**](https://kb.trackiq.com/hc/knowledge-base/articles/what-aria-can-and-cannot-answer) — the boundaries.
- [**MCP Call Logs: Seeing What Your AI Tools Asked**](https://kb.trackiq.com/hc/knowledge-base/articles/mcp-call-logs) — the other audit trail.