Traditional in-app guidance is usually authored flow by flow. Teams decide which steps to show, attach each step to an interface element and maintain the experience when the product changes. Automatic generation replaces part of that manual process with product knowledge and context.
What does automatically generated guidance mean?
It means the system can produce or select useful guidance from its understanding of the software instead of requiring a team to manually script every possible path in advance.
The process can be summarized as:
User goal → product knowledge → relevant workflow → role and context → interface step
Step 1: build structured product knowledge
The system first needs a dependable model of the product. That can include features, functional flows, roles, permissions, dependencies and interface relationships.
Without this layer, an AI assistant may be able to explain a feature but still struggle to guide a user through the correct sequence.
Step 2: understand the user’s goal
The user might ask, “How do I release this shipment?” or “Where can I change the approval limit?” The assistant needs to map that natural-language goal to a specific functional flow.
This is why code-aware product knowledge and workflow detection are useful. They provide a structured set of possible product actions for the AI to reason over.
Step 3: apply role and product context
The correct instructions can change according to role, permissions, workflow state or current screen. Role-aware guidance removes steps the user cannot perform and selects the relevant path.
Step 4: connect the workflow to the interface
Knowing the next functional step is not enough. The system also needs to connect that step to the right route, menu, field, button or control in the application.
This is what turns an AI answer into in-app guidance.
Step 5: present only the next useful action
Long instructions create another reading task. In-app guidance works best when it gives the user the next relevant action, confirms progress and continues through the workflow.
The user stays inside the software instead of switching between the product and a separate help article.
How is this different from generating a product tour?
A generated product tour can still be a predefined sequence. Automatically generated contextual guidance can go further by selecting a path at request time based on the user’s goal and context.
That distinction matters in complex software where there may be many valid paths through the same feature.
The Smartdoxx model
Smartdoxx builds a knowledge layer from the software codebase. That product knowledge can support documentation, AI answers and the in-app assistant.
The aim is to avoid maintaining a separate version of the product truth for every help channel.
What still needs human control?
Automation should not remove product governance. Teams still decide which actions are safe to guide, which content needs review, what tone to use and where users should retain explicit control.
The value of automation is reducing repetitive authoring and maintenance, not removing ownership.
Frequently asked questions
Can AI create in-app guidance without manually building every tour?
Yes, if the AI has structured knowledge of the product and a way to connect workflow steps to the interface.
Does automatic guidance require source-code access?
Not always. Products can learn from documentation, the live interface or usage data. Smartdoxx uses code-derived product knowledge because it provides a direct connection to how the owned software is implemented.
Can automatically generated guidance adapt to different roles?
Yes. When role and permission context are available, the system can select the path that applies to the current user.
Generate guidance from product knowledge
Smartdoxx connects code-derived product knowledge to contextual guidance inside complex software.
See the in-app assistant