RUBIKSCORPAI in SaaS
Why your SaaS AI feature gives wrong answers
Wrong answers are rarely random. The model is usually working from the wrong context, an outdated prompt or data it should never have seen. As your product grows, new users and edge cases pull answers away from what you tested at launch.
Diagnose a wrong answer before changing the model
Start with the complete request path: the user question, the source material retrieved, the prompt version and the final response. A plausible answer can still be wrong for a particular customer, date or product. Record the expected answer and its evidence before adjusting anything. Otherwise, a prompt change may simply make the wording sound more confident while leaving the factual error in place.
What to check
- Collect a small, permissioned set of actual questions, including ambiguous requests and questions the system should decline.
- Check whether each answer is supported by the material available at the time. Mark unsupported claims separately from missing information.
- Group failures by retrieval, instructions, permissions and workflow. Review high-impact errors first.
What to change
Change one part of the system at a time. If the required information is missing, improve the source or retrieval step. If it is present but ignored, inspect the instructions and context length. Add a safe escalation path for questions the system cannot answer. Test the entire journey again, including the message a user sees when an answer is unavailable.
How to evaluate the result
Track factual correctness, evidence support and the proportion of questions that need human help. Keep a separate result for critical topics such as pricing, refunds and access permissions. An overall average can hide a serious problem in a small category.
What the result does not establish
A successful test set demonstrates performance on those cases. It cannot establish that every future answer will be correct. Keep examples of new failures and add them to regression tests.
Further reading
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