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Hi Hann Chiad Hooy,

From my understanding, the answer depends on whether the products follow the same process or lead to different outcomes.

As a general rule:

• Same process, different product → One intent with a Product slot.

• Different process, different outcome → Separate intents.

If Outlook, Teams, Word, and Visio all follow the same support journey and you simply need to identify which Microsoft product the user is referring to, then a single intent with a Product slot would typically be easier to maintain and scale. It also helps avoid multiple intents with very similar training phrases.

However, if each product requires different troubleshooting, knowledge articles, or routing logic, then separate intents may provide better control.

Hope this helps.

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Phaneendra
Technical Solutions Consultant
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Hello, @Hann Chiad Hooy.

Just adding, I would avoid using only one-word utterances like "Outlook", "Word", "Teams", or "Visio" as the main training examples. They can work in some cases, but they can also create ambiguity and false positives, especially with words like "Teams" and "Word", which can appear in normal sentences with different meanings.

In my opinion, the design depends on what you want to do after identifying the product.

If all Microsoft Office products follow the same path, I would keep one intent, something like "Microsoft Office Products", and use slots to capture the specific product, such as Outlook, Word, Teams, Visio, Excel, etc. Then you can route or personalize the response based on the slot value.

But if each product has a very different process, different troubleshooting steps, different routing, or different teams responsible for support, then separate intents may be cleaner, like Microsoft Outlook Support, Microsoft Teams Support, Microsoft Word Support, and so on.

Either way, I would train the bot with full user phrases, not only product names. For example: "I need help with Outlook", "Teams is not opening", "I cannot access Word", "I have an issue with Visio". That gives the model more context and usually improves intent detection.