As Gen is a natural language model, it will understand what you are trying to achieve even if you do make the odd spelling mistake. However, Gen will not like characters such as ‘[' or '>' as these are not generally used in conversational text.

Where you have a large dataset, you are likely to get a deeper insights by selecting a specific Question. Selecting ‘All Questions' will factor in more than may be required.

Further, being specific always helps. Rather than asking ‘What are the main complaints people had?’, ask something like ‘What are the main comments people made with respect to pricing?’. By saying the word ‘pricing’ in the question, Gen is able to apply a hybrid search that picks up the word pricing, and everything related, allowing for a better response.

As an example, I want to know about what’s really driving peoples opinion about the prices at Walmart.

OK: Why?

Good: Why did people say that?

Better: Why did people say that about pricing?

Best: Why did people have those concerns about pricing at Walmart?

Gen has its own context builder and should be able to handle most questions it receives. However, it is helpful for to be as specific as possible.

Gen hasn't been specifically designed to pull quotes from transcripts but there is varied success with this. Try asking "Can you give me a direct quote about X topic", or "Can you give some examples of what respondents have said about Y topic?"

Gen will aim to find responses/comments related to your question in order to give an insightful answer.

As with the above, there is varied results with asking about specific transcripts in your dataset. Try referencing the respondent name if it is in the transcript, or the file name.

As an example, 'Can you tell me what *respondent name* said about *topic*?' or 'Can you tell me what respondents are saying about *Y topic* in the file 'interview.3'?'

Gen will aim to find responses/comments related to your question in order to give an insightful answer.