The Wall Street Journal built a chat product, Lars, the AI taxbot, to answer readers’ questions on filing U.S. taxes. 

The Journal’s reporters write guides to how to file taxes “and every time they write one of these articles, they get hundreds and hundreds of questions with very specific and very personal details.

We obviously can’t answer every one of them. But AI can. This is a great application to get really personal, custom answers,” said Tess Jeffers, director of newsroom data and AI.

“So the motivation is to better leverage our archives and super serve our audience in this lane of coverage that we absolutely want to own.”

Lars is a RAG model, grounded in WSJ content and also publicly available content from the tax authorities. It went through two phases of evaluation: the first internal and then to assess whether the audience found it useful. 

“The first thing all your readers are going to do is try to break your chatbot” to embarrass you, Jeffers said. 

How easy was it to break the bot? 

“This was our second chatbot in six months, and the RAG models are getting so good that we were really impressed,” she said, pointing out that it was much easier to keep Lars on topic and pull in pertinent sources. (The Journal’s previous chat product, Joannabot, was aimed at answering questions about the new iPhone last fall, but it could be tricked into making inflammatory statements or talking about movies or writing code.)

The team will soon need to deploy a new model to prevent drift because the old one is reaching the end of its life. 

What does Jeffers find genuinely useful? “I’m happy we have a workflow editor,” she said, pointing out that this role turned out to be essential to operationalising GenAI.

If you’d like to subscribe to my bi-weekly newsletter, INMA members can do so here.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .