The trick to better answers from generative AI

Generative AI offers great potential as an interface for enabling users to query your data in unique ways to receive answers honed for their needs. For example, as query assistants, generative AI tools can help customers better navigate an extensive product knowledge base using a simple question-and-answer format.

But before using generative AI to answer questions about your data, it’s important to first evaluate the questions being asked.

That’s the advice Lucky Gunasekara, CEO and co-founder of Miso.ai, has for teams developing generative AI tools today.

Miso.ai is the vendor partner for the Smart Answers project here at CIO.com and four of our sister sites. Smart Answers uses generative AI to answer questions about articles published on CIO.com and Foundry websites Computerworld, CSO, InfoWorld, and Network World. Miso.ai also built a similar Answers project for IDG’s consumer technology websites PCWorld, Macworld, and TechHive.

Interested in how Smart Answers surfaces its insights, I asked Gunasekara to discuss more deeply Miso.ai’s approach to understanding and answering users’ questions.

Large language models (LLMs) “are actually much more naive than we may think,” Gunasekara says. For example, if asked a question with a strong opinion, an LLM will likely go off and look to cherry-pick data that confirms the opinion, even if available data shows the opinion is wrong. So, if asked “Why did Project X fail?”, an LLM might scare up a list of reasons why the project was bad — even if it was a success. And that’s not something you want a public-facing app to do.