When a chatbot cannot find an answer, start with one actual question and the sentence that should resolve it. Do not change the document, configuration and question wording at the same time, because you will lose the evidence needed to identify the cause. A missing reliable match differs from an unsupported generated answer: a cautious assistant can decline precisely because it did not find enough knowledge.
When a chatbot cannot find an answer, separate outcomes
A missing match means the system did not find material sufficient to answer the particular question. Record the exact outcome, question and test location. Do not call every refusal a model failure: a question about a private order may fall outside public knowledge. If the assistant supplied a false fact, use the separate guide to chatbot hallucinations rather than treating the two situations alike.
In Meet AI, a NO_MATCH result ends the attempt without asking the model provider to generate an answer. This prevents filling a knowledge gap with invented information, while still leaving the underlying cause to investigate. Separate that result from a provider problem, quota limit or unavailable application. Editing a document cannot solve a state in which the test itself is blocked by access requirements.
Locate the sentence that answers the question
Diagnosis begins by finding the answer in the source material. For “Do you offer appointments on Saturdays?”, locate the schedule or hours for that particular service.
A general company description is insufficient even if it includes the word Saturday. If a person must also guess, you have a content gap rather than evidence that search is malfunctioning. Identify this before changing technical settings.
Copy the relevant passage together with its heading and qualification. “Monday to Saturday” may refer to office opening rather than on-site visits. The guide to choosing data for chatbot knowledge covers source selection. During this investigation, do not expand the collection until you have confirmed that the existing material genuinely answers the question and defines the scope in which its statement applies.
Check the source, site and active publication
A document must belong to the correct site and active knowledge to support its answers. Inspect the source name, origin and publication state. A file visible in a panel is not proof that its current contents power the test. Likewise, changing a website does not automatically refresh a previously imported copy, so compare the versions instead of relying on the latest public page alone.
Scroll the table sideways to see all columns.
| Item | Decision | Check |
|---|---|---|
| Missing source | Check the correct site and its approved source list. | Add approved material rather than copying an entire archive. |
| Inactive change | Compare the current document with the published knowledge version. | Complete publication, then repeat the original question. |
| Unreadable text | Inspect the imported sentence together with its conditions. | Prepare readable material instead of adding identical files. |
| Broad question | Separate the service, variant and information requested. | Clarify context without feeding the assistant the answer. |
Meet AI publishes knowledge through a separate process: the previous generation remains active until the new one succeeds fully. Following a failed publication, answers may therefore still reflect old material. Do not delete a working collection merely to force a change. Record the intended version and operation outcome, then update deliberately using the guide to maintaining current chatbot knowledge.
For a version check, suppose the current document gives a 45-minute consultation while the private test cites an earlier 30-minute duration. Before changing the question, check whether the new document was published and whether the old one remains in the collection. Compare citation and version rather than counting additional generations. Do not remove a condition or source merely because the test used the wrong material.
Inspect readability before expanding the import
Check readability using the actual passage needed for the answer. In a PDF, inspect whether a condition exists only in an image or a detached footnote.
In a table, check that values retain their column headings. On a webpage, establish that the service description has not been confused with navigation, footer content or a list of other offers. The presence of the file alone proves none of these details.
If the relevant sentence was lost during import, prepare an approved text version. Preserve meaning by keeping the exception and scope beside the answer. A clear heading and full product names can help with a long document that uses internal abbreviations. Avoid repeating the same sentence across ten separate files; a later policy change would require finding and correcting every copy before knowledge becomes consistent again.
Repeat the question with necessary context
Context identifies the offer but should not suggest the expected answer. Compare “How long does it take?” with “How long is an introductory consultation?”. The second identifies the service without providing a duration. If only the full name works, check whether the source includes terms customers naturally use and whether earlier conversation messages are pointing the discussion at another service or a different policy.
Use a private Meet AI test to check the question without publishing a conversation to a customer and inspect the cited sources. An account, active application and appropriate access are required. Change one variable at a time: question wording first, then material. Tests share the generation allowance with other AI functions, so read the source and first result before launching repeated identical attempts.
Separate a multi-part question without changing its facts: test “Are Saturday consultations available and what do they cost?” as two individual questions as well. If only one part has source support, do not expect a complete answer to both. Record the confirmed intent before deciding whether content needs adding. This distinguishes missing price information from a failure to find an existing schedule.
When a message could describe two different issues, use the guide: Chatbot clarification questions: ask once before guessing.
Record the cause and accept the correction
Accept a correction using the original question that revealed the issue. If you changed material, compare the result after successful publication rather than only after saving the file. Record a specific cause, such as “The Saturday appointment condition remained in an unreadable footnote.” This helps distinguish one defective source from a recurring format problem across company documents and makes the next investigation faster.
Microsoft documentation on retrieval describes finding content and returning source references. Our diagnostic process is a proposed workflow; it does not present Meetlead as an Azure integration.
If content and publication are correct, prepare a support case containing the exact question, source, relevant passage and test outcome. Remove personal data and do not include access secrets. Broad coverage belongs in the guide to testing a chatbot before launch. This investigation needs evidence that a particular fact should be discoverable but still does not support the response.
If the affected material supports operators, separate the knowledge correction from checking an individual draft. The daily workflow is covered by approving AI Copilot replies; even after fixing retrieval, the suggestion still needs review before the operator sends it manually to a customer.
Questions about missing answers
Should you lower the match threshold? Do not start there.
First confirm that the answer exists in a readable active source and fits the question scope. A less restrictive search may admit material only loosely related to the request. The solution owner should assess technical settings with concrete examples rather than using one broad refusal as proof that the threshold is wrong.
Does uploading the file again help? A copy can hide a version or readability problem while complicating maintenance. Inspect the existing source and publication, then address the cause. If replacing a document, deliberately retire its earlier version so two similar sources do not provide different conditions for the same service or leave colleagues uncertain which one is authoritative.
Does a missing answer consume the Meet AI allowance? NO_MATCH without a reliable passage does not call the provider and does not count as a generation. An answer actually generated in a private test uses the shared AI allowance. Distinguish these outcomes and inspect access state instead of assuming every started attempt has the same cost or the same underlying failure.
