Evidence · first-party tested/Query Live Databases Using Plain English with AI

It presents the order pipeline as a short, scan-friendly summary with stage counts and percentages, making the 93-order breakdown easy for a non-technical user to read.

✓ Worked🧾 artifact-verifiedinput + output shownTest date not recordedBasedash →
What was measured
Result Readability

Is the answer easy for a non-technical user to understand?

transformation

What was given, what came back

Test input: Order pipeline breakdown with paid-pending edge case and last-month comparison · text · group: ecommerce-nl2sql-benchmark
Input — what we sent
The exact prompt
How many orders do we have at each stage right now?

Follow-up 1: What percentage of our orders were successfully delivered vs cancelled?

Follow-up 2: Are there any orders that are pending but already paid?

Follow-up 3: Compare that to last month — same breakdown, I want to see if things have improved or got worse.

A deeper operational analysis of current order stages, delivery-versus-cancellation rates, pending-but-paid edge cases, and a month-over-month comparison of the same breakdown.

Why this input is hard
  • · Order pipeline analysis
  • · Percentage calculation
  • · Edge-case detection
  • · Payment/order status joins
  • · Multi-turn context retention
  • · Month-over-month comparison
  • · Ambiguity handling for 'same breakdown'
Output — unretouched
Output 1
Output 1
Output 2
Output 2
Output 3
Output 3
Output 4
Output 4
Provenance
Observation
17178d7f-4ff3-4043-ab27-94fa7cd18d7e
Evidence run
db2bb5d5-0e0e-4cb3-8d76-3555c45c23cd
Study
Query Live Databases Using Plain English with AI
Research task
86b9y6c99
Tested at
not recorded
Source
first-party
Evidence state
verified
Proof shown
input + output shown
Cost / latency
not captured
Repeat run
not captured
Tester
not captured

The last three rows are honest blanks, not placeholders — our capture has no field for them yet.

Query this
get_evidence({
  tool: "basedash",
  scenario: "ecommerce-nl2sql-benchmark"
})
MCP · mcp.aidemos.com/api/mcp
JSON · ai.aidemos.com/v1/observations/17178d7f-4ff3-4043-ab27-94fa7cd18d7e
Free with attribution.
Same input, same check — 3 other tools
measured on Result Readability
AskYourDatabase✓ WorkedReturned a clean 7-row status table with 93 total orders and an easy-to-scan delivered-versus-cancelled percentage summary.Definite✓ WorkedThe order pipeline outputs were presented in compact tables with totals and percentages, making the 93-order breakdown and the April-vs-May comparison straightforward to read.Querio✓ WorkedPresents the operational breakdown in compact tables with counts and percentages, making the current-stage and month-over-month results easy to scan.
This evidence is published in
Query Live Databases Using Plain English with AI →
From the same study (page rebuilt from a later run)
Best AI Tools to Query Live Databases Using Plain English →
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com