What you will build: Create a small set of known-answer checks for your recurring analysis.
Separate repeatability from accuracy
A reliable reporting workflow should reproduce a measured result when the relevant records, definitions and calculation rules remain fixed. Accuracy asks a different question: is that result correct? A process can repeat the same wrong answer. Test both before relying on a result.
Do not judge reliability by asking for identical prose. Generative wording and qualitative interpretation may vary. A numerical count should come from an inspectable calculation on the defined records, while a proposed theme or score requires evidence and review.
Write the expected answer before testing the assistant
Fictional service example. There are 200 eligible accounts, 120 unique responding accounts and 84 respondents reporting resolution. The response coverage is 120 ÷ 200 = 60%. Resolution among respondents is 84 ÷ 120 = 70%. The remaining 80 eligible accounts have no usable response; they are not automatically classified as unresolved.
Save these expected values with the dataset version, eligibility rule, period and duplicate-handling rule. Ask the assistant to show the numerator, denominator and calculation. A fluent answer that uses 84 ÷ 200 to describe resolution among respondents fails the test.
Build a small regression set
- Choose ordinary records and known exceptions.
- Record the correct answers and supporting records.
- Include missing responses, duplicates, corrections and changed definitions.
- Run the questions using fixed inputs and compare the results.
- Investigate differences before approving an output.
- Repeat the checks after relevant data, model, prompt, tool or rule changes.
This set is a repeatable quality check, not proof that every future answer will be correct. Expand it when a new failure reveals an important case you missed.
Review qualitative interpretation separately
A codebook defines the themes and their boundaries. For example, “setup difficulty” may include unclear instructions but exclude a delivery delay. Keep examples of both. Review ambiguous and ordinary responses, not only the ones the system flags.
When the definition changes, retain the earlier coding version and review the new results before replacing a released finding. Applying the codebook across a large dataset can reduce manual work, but consistent application and sound interpretation still require evaluation.
Use the detailed controlled-calculation guide when you need more depth →
Evaluate the complete workflow
Some general AI tools can execute code, query structured data and show sources. The relevant comparison is whether your team can maintain the full recurring workflow: fixed inputs, clear definitions, inspectable calculations, permissions, reviewed qualitative fields and correction history.
For Sopact, test those requirements on your actual workflow rather than assuming that a data dictionary or source citation alone guarantees correctness. Where permissions allow, verify that the returned source records match the reported total.
Practice and continue
Create five test questions: response coverage; resolution among respondents; a duplicate account; a missing response; and a corrected response. Write the expected answer and rule for each. Carry this test set into the traceability lesson, where you will make one reported claim easy to inspect.
Watch the related explanation
A related Sopact explainer. Use the lesson to distinguish repeatable calculations from generative interpretation.