> For the complete documentation index, see [llms.txt](https://docs.opendialog.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.opendialog.ai/opendialog-platform/conversation-designer/conversation-design/conversational-patterns/information-collection/using-in-opendialog.md). # Using in OpenDialog {% hint style="info" %} As you learn about how to implement different patterns in OpenDialog please keep in mind that OpenDialog is a flexible model that can support a variety of different implementations. We urge conversation designers to use the indications below as a means to learn, be inspired or be unblocked but not as the canon of how things should be done in OpenDialog! {% endhint %} The information collection challenge has been met by many technology companies with slot-filling approaches, but it is not the only way. Google’s Dialogflow, for example, has implemented slot-filling as a dedicated component where one must fill the slots before continuing, an experience separate from the rest of the conversation. By contrast, OpenDialog treats slot-filling as just another contextualized variation on conversation, where each slot is a (required) exchange within a conversation or scene. A benefit of the OpenDialog approach to information collection is that the conversation can support user questions outside of the slot-filling paradigm while information is being collected, and maintain the context. For example, say the user has said they want a large pepperoni pizza but hasn’t yet said what type of crust. While the system may be expecting the next slot to be filled, the user may have a seemingly unrelated question about the restaurant’s hours or delivery time or something else. To be able to handle such unexpected questions, the technology needs to be able to change context to answer the question and then pick up the information collection sequence where they left off. OpenDialog handles this gracefully with the contextual structure inherent to its architecture and the use of “virtual intents”. Within OpenDialog you have a design choice to implement this collection pattern in a single scene or as a collection of scenes (with each piece of info as a separate scene). “Would you like a sweet or dry wine” – could be a single turn or a scene, depending on how much you need to do to “fill that slot”. A doc may need to explain this design choice in OpenDialog. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.opendialog.ai/opendialog-platform/conversation-designer/conversation-design/conversational-patterns/information-collection/using-in-opendialog.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.