We created a WikiBot service that learns from documentation and advises users in chat as a first-line support specialist. Our chatbot understands the question and answers like a human.
The whole world is moving towards the use of AI assistants. People like to get results just by giving commands in their native language.
I suggest you, together with my team, create a Dashboard.AI product that will have the following functionality:
Search reports. The list of reports is usually located in a knowledge base, documentation, or data catalog. Modern products such as SiteGPT, ChatBase, WonderChat, and Wikibot (from now on referred to as the agent) are excellent at performing a semantic search:
Modern LLMs already write SQL quite well ( an article comparing GPT, Claude, Bard ); to do this, they must ask a question and promptly pass a description of the tables. Having received a user request, the agent can take the names of the tables from the documentation and all the fields of the table from the DBMS metadata.
The following projects are currently gaining momentum:
GPT, having received the data table, can already select from it the most significant and suitable
columns for visualization. A comprehensive example of how to do this is described in the article “Create AI-powered dashboards.” By adding to this dashboard templates various best practices in the form of heuristics and clarifying queries from the bot, you can get a Junior BI developer.
In the implementation section, I described what can be done now.
Here are a couple of ideas for the future:
Also Read: Limitations Of Current AI Models
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