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

The first comparison query in the order-pipeline flow hit a small SQL error and had to be fixed and rerun before the answer was produced.

⚠ Struggled🧾 artifact-verifiedinput + output shownTest date not recordedBasedash →

What was measured

SQL Generation

Does it generate database-backed SQL correctly?

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

Also checked on this input — same tool, 1 other criterion

Result Readability✓ WorkedIt 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.

Provenance

Observation: 8c407944-3938-4134-9cb8-0246c5dd5fdf
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/8c407944-3938-4134-9cb8-0246c5dd5fdf

Free with attribution.

Same input, same check — 2 other tools

measured on SQL Generation

AskYourDatabase✓ WorkedHandled the month-over-month follow-up with multiple SQL statements to compare the current snapshot, inspect paid-but-pending orders, and build the prior-month comparison.Definite✓ WorkedIt correctly produced the order-pipeline result set across the multi-turn flow, including the 93-order stage breakdown and the April-versus-May comparison tables, without manual SQL writing.

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