When Data Requests Pile Up in the IT Queue
"Can you pull last month's sales by customer?" The query behind that request is a few lines of SQL, yet once it passes through intake, prioritization, and a round of result checking, the answer often takes days. Developers get pulled off their real work several times a day, and business users give up waiting and build the numbers by hand in Excel.
Text-to-SQL shortens that queue by turning a natural-language question into SQL. Its scope, however, needs to be drawn clearly.
Where Text-to-SQL Goes Wrong
The Gap Between Business Terms and the Schema
Users say "customer," but the tables are called `CUST_MST` and the key is `BP_CD`. "Sales" may mean booked orders, confirmed shipments, or issued tax invoices, depending on the company. The model fills that gap with a guess, and the guess becomes a plausible wrong answer.
SQL That Runs but Means the Wrong Thing
The more dangerous failure is a wrong answer that executes without error.
Because a number comes back looking perfectly normal, the user has little chance of noticing it is wrong.
Designing for Accuracy
Three Kinds of Context to Provide
Read-Only Views Instead of Raw Tables
Rather than exposing hundreds of raw tables, it works better to expose only 10 to 20 read-only views organized around business concepts, such as `v_sales_monthly`. When the joins and the exclusion of cancelled orders are handled inside the view, the model has far less room to build a bad join, and sensitive columns can be left out from the start.
Guardrails You Cannot Skip
Making Results Trustworthy
A bare number does not build trust. Show the generated SQL along with a plain-language reading of it, such as "Aggregated for September 1–30, 2026, on a confirmed-shipment basis, excluding cancelled orders," so users can check the assumptions themselves.
Quality is maintained through regular evaluation. Build a golden set of around 100 real questions paired with correct SQL, and re-score it by comparing result values whenever the prompt, the model, or the schema changes.
When a question is ambiguous or asks for a metric that is not in the glossary, the system should ask a clarifying question or hand it off to a human owner instead of forcing an answer. Those handed-off questions then become the raw material for improving the glossary and the example queries.
How POLYGLOTSOFT Helps
Whether Text-to-SQL succeeds depends less on the model you pick than on how well the data has been understood and organized. At POLYGLOTSOFT, a team that understands ERP, MES, and WMS data structures builds the whole pipeline with you: designing read-only views, writing the business glossary, setting up permissions and validation, and evaluating against a golden query set. If you want to shrink your data request queue, get in touch with POLYGLOTSOFT.