Executive summary
A working AI-generated application still needs verification. Learn practical checks for accurate results, duplicate records, permissions and recovery.
AI-assisted development can help turn an idea into a working application faster. But a successful demonstration leaves important questions unanswered. Does the application calculate correctly? Does it handle a failed upload? Can someone see another client’s information? A working demo is the beginning of verification.
For a small business, these questions connect directly to everyday decisions. A reporting tool that duplicates transactions can distort sales. An automation that loses rejected rows can leave an incomplete report looking complete. Start by defining what the tool should do, then collect evidence that it does it.
1. Define the result before checking the code
Imagine a tool that imports orders from a spreadsheet and produces a sales summary. Write down the acceptance rules: each order appears once; invalid rows are explained; totals match accepted records; and each client sees only their own information. Decide what should happen when an existing order is corrected.
Use a small fictional dataset with an independently calculated answer. Three valid orders worth R100, R200 and R300 should produce R600 in sales. Add a duplicate order and a row with a missing amount. Specify whether those rows should be rejected, flagged or handled another way. A missing value should not silently become zero unless that is an explicit business rule.
Ask the AI assistant to explain its calculation and propose tests, but compare the output against your expected result. Code and tests generated from the same misunderstanding can agree with each other while answering the wrong business question.
2. Try inputs that real users will supply
Test an empty file, a missing column, an invalid date and an amount stored as text. Decide how ambiguous dates such as 03/04/2026 should be interpreted. A useful error message explains what failed and how to correct it without exposing private information.
Account for every row. If a file contains 100 rows, the accepted, rejected and separately classified rows should reconcile to that input under the stated rules. If five rows need correction, make them visible. A green success message should not conceal missing records.
3. Check duplicates and retries
Upload the same file twice. Click submit twice. Retry after an interrupted request. Does the tool recognise completed work or create another copy? For the order example, choose a reliable order identifier and an explicit rule for duplicates. Matching on a name alone can merge different people. Matching on every field can miss a duplicate after a small edit.
Decide whether importing a corrected order should update the existing record or create a new version. Then test that behaviour. The rule must reflect the business process.
4. Verify permissions with separate test users
Create two test users with separate sample records. Confirm that one cannot read, change or download the other’s data, including by opening a copied record link. Hiding a button is not an access-control test. The server must enforce permission when the request arrives.
Use fictional or appropriately de-identified examples when working with AI tools. Check the tool’s data-handling arrangements before supplying client information. Keep passwords and secret keys out of prompts, public code and browser-delivered application files. For sensitive information or important business processes, involve a suitably experienced reviewer.
5. Make recovery part of the design
Interrupt a test import and inspect the saved records before retrying. Does the application roll back the incomplete operation, or resume safely from a known point? Either approach needs an explicit design and a test that demonstrates it.
Before changing real data, confirm that backups exist and test restoring a copy in a safe environment. Keep error records useful enough to investigate failures while avoiding unnecessary personal information or secrets. Assign responsibility for reviewing failures and resolving rejected records.
6. Release in stages and keep checking
Start with sample data, move to a limited pilot and compare results with the existing process. Keep the previous process available until the new one has earned trust. A successful build, passing automated checks and a polished interface each provide evidence, but none alone proves that the whole workflow is correct.
For the order-import example, a practical release checklist is
- The independently calculated R600 total reconciles.
- Invalid rows are visible and accounted for.
- Uploading twice does not double sales.
- Two users remain separated.
- An interrupted import can be recovered.
- Someone can explain how the result was produced.
Adapt these checks to the consequences of a mistake in your application. Re-run the relevant checks when the code, inputs or business rules change.
Turn a working demo into a dependable business tool
AI can accelerate development. Understanding the problem, checking the results and taking responsibility for the solution remain part of the work. Ask: what evidence would make you comfortable relying on this tool tomorrow?
The Spokesdude Network helps organisations clean, analyse and report on their data, and improve the processes that move information from source to decision. If your team relies on repetitive spreadsheets or reports that do not reconcile, explore our solutions or contact us to discuss the workflow you want to improve.