The output stays readable for non-technical users, using compact tables and short summaries with explicit customer names and amounts.
What was measured
Result Readability
Is the answer easy for a non-technical user to understand?
decisive for this rankingtransformation
The point is to get usable answers from non-technical users, so the result has to be understandable. (3 of 3 judges)
What was given, what came back
Test input: Best customers with unpaid-order and payment-method follow-ups · text · group: live-database-plain-english-queries
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 with follow-up questions. It asks for the best customers by both order volume and spend, then drills into unpaid orders for the top 3 and their usual payment methods. Designed to test ranking logic, join-heavy analysis, and follow-up context retention.
Why this input is hard
- · ambiguous business term interpretation
- · multi-table joins
- · aggregation and ranking
- · follow-up context retention
- · scoping to a selected subset
- · payment behavior analysis
Output — unretouched
Also checked on this input — same tool, 5 other criteria
Ambiguity Handling⚠ StruggledIt did not ask what 'top 3 from that list' meant; instead it silently chose the spend ranking first and then added the order-count check.Business Insight✓ WorkedIt explains what the numbers mean by naming Rahul Sharma as the best overall customer, Mohan Vishe as the most frequent, and Deepak Kulkarni as the biggest spender.Follow-Up Context◐ MixedIt remembered enough of the prior answer to check both ranking lists, but it still narrowed the follow-up instead of preserving the user's intended scope cleanly.Plain English Query Handling✓ WorkedIt correctly handled a conversational, multi-part customer question without requiring SQL, producing two rankings plus follow-up answers.SQL Generation✓ WorkedIt successfully generated the ranking queries and follow-up lookups, including the top spenders, top order-count customers, unpaid-order status, and usual payment methods.
Provenance
- Observation
- 8631a190-2b78-4695-a457-2b0ac633578c
- Evidence run
- af2abc96-3311-484b-a19d-854a2fdd2bf3
- 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: "live-database-plain-english-queries"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 5 other tools
measured on Result Readability
AskYourDatabase✓ WorkedIt presents the answer as clearly labeled ranking tables and customer-level payment summaries, with visual risk cues for unpaid or shipped orders.Definite✓ WorkedIt returns a ranked table with order counts, total spend, and average order value that is straightforward to read and scan.Dot◐ MixedThe follow-up answers are readable but terse: each is a one-line response, while the supporting table and SQL are pushed out of the main surface into Full logs.Draxlr⚠ StruggledAll three best-customer result tables keep customer_id as the lead column, so the output stays UUID-heavy and hard to scan.Querio◐ MixedThe answer is usable, but it is less readable than it could be because the output centers customer UUIDs instead of human names.
This evidence is published in
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