What happens if iDBQuery gets a question wrong?
If iDBQuery misreads a question, the citations and visible SQL make it easy to spot, because you can see exactly which rows and logic produced the answer. You can then rephrase, add detail or correct the query, and it self-checks its work to reduce mistakes.
No AI is perfect, and iDBQuery is designed so that when it misinterprets a question, you can catch and correct it rather than being misled.
- It is visible when it happens. The answer comes with the SQL it ran and citations to source rows, so a wrong interpretation shows up as the wrong filter, join or date range — not a plausible-sounding but untraceable number.
- It asks when unsure. For ambiguous questions, iDBQuery clarifies or states its assumptions rather than silently guessing.
- It self-checks. The Analyst agent reviews its own steps before presenting a conclusion.
- You steer it. Rephrase, add a detail ('by fiscal year, not calendar'), or have a technical user tweak the generated SQL, and it corrects course.
For example, if you ask 'sales last month' and mean the fiscal month, the citation will reveal it used calendar dates — you spot it instantly and clarify. Contrast that with a chatbot that states a wrong figure with no way to trace it. The point is not that iDBQuery never misreads a question; it is that mistakes are transparent, correctable and rarely make it into a decision unnoticed. Transparency is the safety net.
Updated 2026-06-22