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 wrote:

I've been thinking about this while looking at different Genie use cases.

It seems like the hardest part of implementing Genie isn't getting users to ask questions in natural language. It's making sure Genie understands what those questions actually mean in the context of the business.

For example, if someone asks:

“Why did our performance drop this week?”

Genie still needs to know:

• Which metric are we talking about?
• Which data is authoritative?
• How are the tables related?
• What does “performance” actually mean for that business?
• Are there business rules that should affect the answer?

This becomes even more important for operational use cases. Questions around NPT, ROP, equipment performance, or cost can require data from multiple systems.

That makes me wonder if we're putting too much emphasis on the conversational interface and not enough on what sits underneath it.

Maybe the real Genie implementation project isn't building the Genie space. website It's building the context that allows Genie to give a trustworthy answer.

Curious what others are seeing:

What has been the bigger challenge in your Genie projects — configuring Genie itself, or getting the underlying data, metrics, and business definitions ready for it?

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I agree that the semantic layer is probably the part that requires the most careful planning. A natural language interface can make querying easier, but if the underlying metrics and business definitions are inconsistent, Genie can still produce an answer that sounds correct but is based on the wrong context. Starting with one well-defined business area and expanding gradually seems like a much safer approach.