How to reduce chatbot hallucinations and wrong answers
Diagnose whether a wrong answer came from content, retrieval, instructions or the model before applying a fix.
“Hallucination” often labels several different failures: the fact is absent, conflicting, extracted badly, not retrieved or ignored. Each failure requires a different fix.
Start with an exact failing question and inspect evidence before changing model or adding stronger wording to the prompt.
What you will have at the end
- A classified root cause for one wrong answer
- A targeted source, retrieval or prompt fix
- A regression set that catches recurrence
Before you start
- The exact wrong question and answer
- Access to source content and Knowledge Test
- Expected answer approved by a content owner
Trace the evidence path
A grounded answer depends on authoritative source text, correct extraction, useful chunks, relevant retrieval and instructions that require evidence. A stronger model only affects the final stage.
Conflicting current and obsolete sources are especially dangerous because retrieval can legitimately find either version.
01–05
Set it up step by step
Reproduce and capture the failure
Keep question, answer, time and chatbot state.
Reset the conversation, ask the exact question and save the wrong answer with model, prompt version and source status. Do not troubleshoot from a paraphrased memory.
Audit source truth and conflicts
Find missing, duplicated or obsolete facts.
Search all active sources for the expected fact and alternatives. Remove old policy versions, print pages, translated duplicates and contradictory manual text before re-indexing.
Inspect extraction and retrieved passages
Confirm that the model actually received useful evidence.
For PDFs verify selectable text or OCR, table order and headings. In Knowledge Test inspect retrieved passages: missing evidence means content/retrieval work, present evidence suggests instruction/model work.
Add a safe evidence and abstention rule
Tell the bot what to do without support.
Add a concise system instruction to use indexed evidence for company facts, acknowledge missing information and offer the approved escalation path. Do not force an answer for every question.
Run regression and only then compare models
Prove the targeted fix and guard nearby questions.
Retest the original plus related, ambiguous and unanswerable questions. Compare models only if correct evidence is consistently present but still mishandled; record any improvement against quota and latency.
Example & result
See the practical test and its result
Every tutorial includes a fixed input, the expected outcome and a transparent record of what was actually verified locally.
Practical example: How to reduce chatbot hallucinations and wrong answers
This exact scenario was completed with the temporary tutorial account.
Exact test input
What is Northstar Services’ refund policy for annual contracts?
Expected result
The bot says the information is not in the indexed sources instead of guessing.
What was actually verified
The live assistant replied that it could not find a refund policy in the available information and did not invent terms.
Tips & tricks
Make the setup reliable
Test with realistic examples, record your baseline and change one setting at a time. That makes real improvements visible.
Use explicit effective dates
Policies and prices should state version or effective date so current content is distinguishable from archives.
Test safe “I do not know” behavior
An assistant that correctly declines unsupported claims is more reliable than one that always sounds complete.
When something does not work
Troubleshooting
Check status, permissions and test data systematically before changing the model or prompt.
The expected option is missing
Confirm the account plan, feature permissions and selected chatbot. Paid or beta features can be hidden when prerequisites are not met.
The test result is inconsistent
Reset the test conversation, keep the input identical and change one setting at a time so the cause remains measurable.
Ready for a production-style test
Track the failure class for every negative feedback item. Repeated classes reveal which source, extraction or evaluation process needs a systemic fix.