Gen is an AI Research Assistant that allows you to have a conversation with your own data in a similar way to how you use ChatGPT’s technology – all within Yabble’s secure, walled garden data environment. Gen uses a natural language model which means you can ask your data all types of questions and Gen will be able to answer your question instantly and conversationally. While ChatGPT is asking questions of the internet up to 2021, Gen allows you to ask questions on your own proprietary dataset – whatever topic that might be. Once your dataset is uploaded into the Yabble platform you can prompt Gen to query your dataset and provide high-level insights or dive deeper to explore your data holistically – all with data security peace of mind.

Yes, Gen can chat with any type of qualitative data you have in the Yabble platform, including raw transcripts.

While Gen can talk to data that has been theme counted, at present it does not factor in the results of the TC. So the ‘top 3 things people talked about' may be different in some cases. However, it is a future (however more complex) feature we are investigating.

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 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 what’s really driving people's 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 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 are 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.

Gen is a language model, and for the time being, doesn’t like counting. Gen can however give you an answer like ‘most’ or ‘the main topics people discussed'.

Asking Gen an identical/similar question in the same chat will give different responses. Often the subsequent responses can add more detail, as Gen will not want to repeat itself.

The more detail you give, the more information Gen has to work with. However, you can simply ask a question like ‘Why?’ and Gen will go and find context from your previous conversation.

Gen will find responses related to your question in order to give an insightful answer. It may not need your entire dataset before it can give an answer like ‘most people preferred…’

Further, survey data can contain irrelevant answers. Gen will filter these answers out or ignore them in its response generation.

For now, Gen only likes data that is in English. Please watch this space while Gen becomes multi-lingual!