Conversation content quality

Check whether the question fits the topic—and the answer fits the question.

Review the meaning of recorded or transcribed conversations against your topic definitions. AutoQC can configure separate relevance checks for questions and answers, with reasons that help content reviewers understand alignment, drift and ambiguity.

Discuss your relevance checks

A conversation can pass one check and fail another

A dataset assigned to commuting may contain a suitable question about transport but an answer about food preferences. A single conversation score hides that distinction. Separate the two relationships so reviewers know whether to inspect the prompt, the response or the way the segments were paired.

Start with your workflow

The inputs that make the analysis useful

Topics and conversation material

Provide assigned topic IDs, topic definitions and recordings or transcripts. Include the question and answer segments where available, or samples for assessing segmentation.

A relevance rubric with examples

Supply reviewed aligned, off-topic, partial and ambiguous cases. Define acceptable digressions, required coverage and when a reviewer should decide.

Judge the question against the assigned topic

A question can use the expected keywords while asking about something else. It can also fit the topic without using those words. Scope the check around the topic’s meaning and boundaries, including related subtopics that your content specification permits.

Judge the answer against the actual question

An answer may discuss the broad topic yet miss the specific request. If the question asks why someone chooses a travel mode, naming that mode is only part of the response. Configure the distinction between relevant, partial and mismatched answers using your rubric rather than treating every answer as a binary pass or fail.

Keep pairing errors visible

The wrong question-answer pairing can manufacture a mismatch. Evaluate speaker turns, interruptions, follow-up questions and answers spread across several turns before relying on relevance judgments. Unclear segmentation should lead to a pairing-review status, with the relevant surrounding context available to the reviewer.

Use findings to guide content review

Agree a segment-level view with topic, question, answer, judgment and reason. Reviewers can separate a valid paraphrase from topic drift and decide whether a partial answer meets the collection specification. Where the material is an extempore response, scope response-to-topic review separately instead of inventing a question that was never asked.

Illustrative example

One aligned question, two different answer results

One aligned question, two different answer results
Assigned topicQuestionAnswerJudgment
CommutingWhy do you take the bus?Parking near work is difficult.Question aligned; answer relevant
CommutingWhy do you take the bus?My favourite lunch is rice.Question aligned; answer mismatched

Fictional teaching example, not actual client output. The evaluation will establish the required output structure.

Define relevance before scaling review

This is a scoped content-evaluation workflow using supplied material. Agree segmentation, export and review handoff for your project, then compare both checks with reviewed labels. Relevance describes whether content addresses the topic or question; it does not verify factual truth or establish the speaker’s expertise.

Questions about this workflow

Can a digression still be acceptable?

Yes. A brief side point may explain the answer, while a longer unrelated passage may not. Your rubric defines the boundary, and representative examples help reviewers apply it consistently.

How is the evaluation assessed?

Review question-to-topic and answer-to-question agreement separately. Include paraphrases, partial responses and uncertain pairings, then examine disagreements and supporting reasons before expanding the workflow.

Related use cases

01 / Research operations

Research fieldwork quality control

Your fieldwork batch is ready for processing, but checking a few interviews leaves questions unanswered. Did the respondent meet the study criteria? Does the entered answer match what they said? Was a required probe used? Bring those questions into one study-specific review, with the record and question identifiers your team already uses.

Explore this use case: Research fieldwork quality control
02 / Qualitative research

Qualitative research analysis

Your interviews are complete, but the insight is scattered across long conversations. You need to compare experiences, understand why respondents differ and build an argument for the study objectives. Start with the guide and the analysis grid you want to work in, then connect each finding back to the relevant source.

Explore this use case: Qualitative research analysis
03 / Process and conversation quality

Call auditing against your SOP

Your agents serve different call types, products and customer situations. A disclosure may be mandatory in one conversation and irrelevant in another. You need an audit that follows those distinctions and gives supervisors a clear reason to inspect a call.

Explore this use case: Call auditing against your SOP

Make your content acceptance rules explicit

Describe the topics, conversation format and review decisions you need. After contact, we will agree sample access and a representative relevance evaluation.

Discuss your relevance checks