{"observation":{"id":"f3de1a0e-2238-4b50-af61-e2215c859818","tool":"definite","tool_name":"Definite","criterion":"follow-up-context","criterion_name":"Follow-Up Context","criterion_definition":"Does the tool remember previous answers correctly?","criterion_evidence_type":"transformation","criterion_rank_role":null,"criterion_rank_role_reason":null,"scenario":"best-customers-with-unpaid-order-and-payment-method-follow-ups","scenario_name":"Best customers with unpaid-order and payment-method follow-ups","group_tag":"ecommerce-nl2sql-benchmark","scenario_description":"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.","modality":"text","input_text":"Who are my best customers — the ones who order the most and spend the most?\n\nFollow-up 1: For the top 3 from that list — do any of them have unpaid orders?\n\nFollow-up 2: What payment methods do these top 3 usually use?","input_artifact_refs":["AskYourDatabase_Best_Customers_SQL_Visible-2.png","AskYourDatabase_Top_Customers_Unpaid_Orders-2.png","Draxlr_Top_Customers_Payment_Methods.png"],"stresses":["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"],"verdict":"worked","score":null,"score_total":null,"note":"It retained context across follow-ups, allowing the user to ask about unpaid orders and then payment methods for the top 3 from the earlier spend-ranked list.","evidence_state":"verified","source":null,"artifacts":[{"url":"https://d3epheqghktydj.cloudfront.net/definite-image-4-aa6da855028f.png","role":null,"alt":null},{"url":"https://d3epheqghktydj.cloudfront.net/definite-image-5-dd956d37b902.png","role":null,"alt":null}],"run_id":"db2bb5d5-0e0e-4cb3-8d76-3555c45c23cd","study_title":"Query Live Databases Using Plain English with AI","study_kind":"generation","research_task":"86b9y6c99","tested_at":null,"completeness":"input-and-output","input":{"state":"text","text":"Who are my best customers — the ones who order the most and spend the most?\n\nFollow-up 1: For the top 3 from that list — do any of them have unpaid orders?\n\nFollow-up 2: What payment methods do these top 3 usually use?","files":[],"modality":"text","stresses":["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"]},"tool_page_slug":"definite","tool_url":"https://aidemos.com/tools/definite","permalink":"https://aidemos.com/evidence/f3de1a0e-2238-4b50-af61-e2215c859818","api_url":"https://ai.aidemos.com/v1/observations/f3de1a0e-2238-4b50-af61-e2215c859818"},"peers":[{"id":"0f379e1b-c980-49c7-9d60-df438430e631","tool":"askyourdatabase","tool_name":"AskYourDatabase","verdict":"worked","score":null,"score_total":null,"note":"Kept the top-3 customer context across the follow-up chain by hardcoding the same three customer IDs into the unpaid-order check.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/askyourdatabase-image-5-7c106e44c3e4.png","evidence_url":"https://aidemos.com/evidence/0f379e1b-c980-49c7-9d60-df438430e631"},{"id":"ae10374c-81bf-48f0-9b48-3a4ef00276c8","tool":"basedash","tool_name":"Basedash","verdict":"mixed","score":null,"score_total":null,"note":"It retained the earlier ranking context only partially: the unpaid-orders follow-up checked the top 3 highest spenders first, then had to run a separate pass for the top 3 by order count instead of carrying one unambiguous 'top 3' thread forward.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/basedash-image-3-cb6d35524fe0.png","evidence_url":"https://aidemos.com/evidence/ae10374c-81bf-48f0-9b48-3a4ef00276c8"},{"id":"f17a5005-134e-4912-8a04-b8fcd44f94dd","tool":"querio","tool_name":"Querio","verdict":"worked","score":null,"score_total":null,"note":"Carries the top-3 customer identities forward across follow-ups and regenerates fresh SQL on each turn, preserving context correctly across at least two drill-down questions.","artifact_count":3,"thumbnail":"https://d3epheqghktydj.cloudfront.net/querio-image-10-74309124c4ad.png","evidence_url":"https://aidemos.com/evidence/f17a5005-134e-4912-8a04-b8fcd44f94dd"}],"other_criteria":[{"id":"0708d68c-6a24-40a7-8711-3ad8d5d5a901","criterion":"ambiguity-handling","criterion_name":"Ambiguity Handling","rank_role":null,"verdict":"failed","score":null,"score_total":null,"note":"It did not clarify the ambiguous 'best customers' request and instead silently narrowed the task to total spend, ignoring the 'order the most' part of the question.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/0708d68c-6a24-40a7-8711-3ad8d5d5a901"},{"id":"6161c827-6469-4922-ac82-0511d300dc87","criterion":"business-insight","criterion_name":"Business Insight","rank_role":null,"verdict":"worked","score":null,"score_total":null,"note":"It added useful follow-up commentary by flagging Rahul Sharma's unpaid $2,199 order as worth chasing because he was #2 by spend, and by linking that unpaid order to his payment behavior as likely an oversight rather than a pattern.","artifact_count":2,"evidence_url":"https://aidemos.com/evidence/6161c827-6469-4922-ac82-0511d300dc87"},{"id":"4a158cea-3703-411a-867f-d7756334ecb8","criterion":"result-readability","criterion_name":"Result Readability","rank_role":null,"verdict":"worked","score":null,"score_total":null,"note":"The ranked customer table was readable and easy to scan, with rank, orders, total spend, and average order value clearly laid out for the top 20 customers.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/4a158cea-3703-411a-867f-d7756334ecb8"}],"appears_in":[{"page_type":"use-case","slug":"query-live-databases","title":"Query Live Databases Using Plain English with AI","url":"https://aidemos.com/use-cases/query-live-databases","binding":"run"},{"page_type":"ranking","slug":"text-to-sql-tools","title":"Best AI Tools to Query Live Databases Using Plain English","url":"https://aidemos.com/best/text-to-sql-tools","binding":"study"}],"same_scenario":[{"id":"0f379e1b-c980-49c7-9d60-df438430e631","tool":"askyourdatabase","tool_name":"AskYourDatabase","verdict":"worked","score":null,"score_total":null,"note":"Kept the top-3 customer context across the follow-up chain by hardcoding the same three customer IDs into the unpaid-order check."},{"id":"ae10374c-81bf-48f0-9b48-3a4ef00276c8","tool":"basedash","tool_name":"Basedash","verdict":"mixed","score":null,"score_total":null,"note":"It retained the earlier ranking context only partially: the unpaid-orders follow-up checked the top 3 highest spenders first, then had to run a separate pass for the top 3 by order count instead of carrying one unambiguous 'top 3' thread forward."},{"id":"f17a5005-134e-4912-8a04-b8fcd44f94dd","tool":"querio","tool_name":"Querio","verdict":"worked","score":null,"score_total":null,"note":"Carries the top-3 customer identities forward across follow-ups and regenerates fresh SQL on each turn, preserving context correctly across at least two drill-down questions."}]}