Handled the ambiguous phrase by not collapsing it into one ranking; it produced separate order-count and total-spend rankings instead of guessing silently.

✓ Worked🧾 artifact-verifiedinput + output shownTest date not recordedAskYourDatabase
What was measured
Ambiguity Handling

Does the tool clarify unclear business terms instead of guessing silently?

transformation

What was given, what came back

Test input: Best customers with unpaid-order and payment-method follow-ups · text · group: ecommerce-nl2sql-benchmark
Input — what we sent
The exact prompt
Who are my best customers — the ones who order the most and spend the most?

Follow-up 1: For the top 3 from that list — do any of them have unpaid orders?

Follow-up 2: What payment methods do these top 3 usually use?

A conversational multi-table customer analysis that identifies best customers by both order volume and spend, then drills into unpaid orders and payment methods for the top 3.

Why this input is hard
  • · Ambiguous business-term interpretation
  • · Multi-table joins across customers orders and payments
  • · Aggregation and ranking
  • · Follow-up context retention
  • · Scoped drill-down to the top 3 customers
  • · Readable customer-level output
Output — unretouched
Output 1
Output 1
Output 2
Output 2
Also checked on this input — same tool, 10 other criteria
Business Insight✓ WorkedGenerated useful takeaways automatically, including Rahul Sharma as the standout all-rounder, Mohan Vishe as the most loyal frequent buyer, and Vikram Singh as the largest spender.Business Insight✓ WorkedThe follow-up reasoning went beyond listing payments by flagging Mohan Vishe's unpaid shipped orders as a process risk and noting that Rahul Sharma's pattern was largely UPI-driven.Chart / Visualization Support✗ FailedVisualization did not auto-generate on this customer analysis flow; the report says it required an additional prompt every time.Follow-Up Context✓ WorkedKept the top-3 customer context across the follow-up chain by hardcoding the same three customer IDs into the unpaid-order check.Follow-Up Context✓ WorkedThe tool preserved conversational context across turns by reusing the previously identified top three customers in the unpaid-order follow-up.Plain English Query Handling✓ WorkedThe tool understood follow-up phrasing naturally, including 'For the top 3 from that list' and 'What payment methods do these top 3 usually use?'.Plain English Query Handling✓ WorkedAccepted an informal customer-analytics question and split it into two dimensions without requiring SQL: who orders most and who spends most.Result Readability✓ WorkedPresented the follow-up results as clearly labeled customer status blocks such as 'All Clear', '1 Unpaid', and 'All 4 Unpaid!', which makes the risk scan easy.Result Readability✓ WorkedThe customer ranking output was easy to scan because it separated frequent shoppers from highest spenders into clearly named tables with totals and ranks.SQL Generation✓ WorkedRan 2 SQL queries simultaneously for the main request, and then generated a filtered follow-up query for the exact top 3 customers.
Provenance
Observation
87bac5d0-cd93-46de-a5fa-5daec5d3d86d
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: "askyourdatabase",
  scenario: "ecommerce-nl2sql-benchmark"
})
MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 2 other tools
measured on Ambiguity Handling
From the same study (page rebuilt from a later run)
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com