I don't promise zero hallucinations. I measure yours and cut them - and you see the number.
Every language model can make things up, and anyone who guarantees none is selling you a story. Here is the honest version, and it is the one that survives contact with your customers: I ground the AI in your own data, make it cite and verify, let it say "I don't know," and then I measure the hallucination rate on your real questions and show it drop.
How I make it trustworthy
Ground itThe AI answers from your approved documents, not its memory.
Show its workEvery answer cites the source, so your team can click and verify it.
Admit uncertaintyWith no solid source it says "I don't know" instead of guessing.
Double-checkA second pass flags any unsupported claim before it reaches a customer.
Measure itI test it on your real questions and report the rate before and after.
The meter - your number, from your data
Run your test set before and after the work, enter the counts, and it computes the reduction you can defend.
Before (baseline)
Test questions
Hallucinated (wrong / unsupported)
Honest "I don't know"
Grounded & correct28
After (your setup)
Test questions
Hallucinated (wrong / unsupported)
Honest "I don't know"
Grounded & correct42
Hallucination rate: → , a cut
relative reduction
grounded & correct (after)
honest "I don't know" (after)
How the number is made (so it holds up)
1. Test set30–100 real questions your users actually ask, each with a known correct answer from your sources. Your expert confirms the answers.
2. BaselineRun your current setup on the set, score every answer.
5. ReportThe change, on your data - the meter above.
Grounded & correct ✓ - right, and supported by a real cited source. This is what we grow.
Hallucination ✗ - states something false, or a claim/citation not in the sources, as if it were fact. This is what we cut.
Honest "I don't know" ✓ - declines or asks for more when there is no solid source. Not an error - a feature.
The honest scope. The number is real but bounded: it is measured on this test set of your questions, which is representative, not every possible question, and it is not a guarantee of zero. Refusals count as honest, not as errors. Have your own expert validate the scoring, or at least spot-check a sample, so the figure is yours and credible. That is the version I will stand behind in front of your customers.
Kenneth W. Bingham · reliability and AI enablement
Concept and direction: Kenneth W. Bingham. Built with the help of Claude AI under a standing directive to be skeptical, to insist on proof, and to allow no claim that is not demonstrated.