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What a chatbot conversation flow should include
A conversation flow is the logic that takes a user from an opening request to a useful outcome. It defines what the bot asks, what information it needs, how it responds to different inputs, and what happens when the user changes direction or gets stuck. The channel and interface—such as text chat, voice, or available input controls—shape how that logic is presented, but they are distinct from the flow itself. IBM discusses this distinction in its chatbot design guidance.
Two artifacts make the design concrete:
- Sample dialogues: written exchanges that let you judge whether prompts and replies are clear, relevant, and natural.
- A diagram of the conversation flow: a map of the main route, branches, and transitions, including detours and recovery paths.
Google recommends iterating between sample dialogues and the flow diagram. Its guide attributes this advice to Cathy Pearl, Head of Conversation Design Outreach at Google: “The easiest way to start writing dialogs is to channel your own expertise as a lifetime communicator.” Google’s conversation design guide also recommends role-playing to draft and refine dialogue.
How to design a chatbot conversation flow
1. Choose one persona and one use case
Start with a single kind of user and a single task. Describe what the person wants to accomplish, what relevant context they may have, and what counts as success. For example, a customer who needs to reschedule an appointment may need the bot to identify the existing booking, find an available time, confirm the change, and report that it is complete.
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Keep the first design narrow enough to reason through from beginning to end. Once it works, repeat the exercise for other user types and tasks. Google recommends beginning with one persona and use case, then expanding to others.
2. Role-play and write the sample dialogue
Have one person play the user and another play the bot; if working alone, switch roles. Write down the exchange as it happens. Treat this transcript as a draft for discovering missing questions and awkward turns—not as polished final copy.
Make the user sound like a person with a goal, not like a form being filled out. Include realistic uncertainty, incomplete information, and the occasional extra question. A useful bot line should either move the task forward, clarify what is needed, answer a relevant question, or explain a next step.
3. Review each line in the intended channel
Read every bot line in the form the user will experience. In voice experiences, Google recommends stepping through user and system lines with the intended text-to-speech voice, then rewriting anything that sounds unnatural. In text chat, check that every prompt is easy to understand, relevant to the current task, and gives the user a useful next action.
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Also consider the interface. A flow that expects a date, for example, may be presented as typed text or a date picker depending on the channel and available controls. The interface can constrain presentation without changing the underlying task logic.
4. Draw the high-level flow
Turn the sample dialogue into a diagram showing the main path, decision points, branches, and transitions. Include the information the bot must retain to continue the task—for example, which appointment the user selected and whether a new time has been confirmed.
Mark both the standard route and meaningful alternatives. If the system supports natural-language shortcuts, show where users can provide information out of order or jump to a later part of the task. Google recommends defined paths while allowing shortcuts when the system can support them.
5. Set boundaries, detours, and recovery behavior
Decide what the bot does when a request falls outside the task, an answer is unclear, required information is missing, or the user changes their mind. Possible responses include asking a clarifying question, answering a side question and returning to the pending task, repeating an earlier prompt, restarting, or handing the conversation to a person. The right behavior depends on the use case; there is no single interruption policy for every chatbot.
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Give stuck users a clear way to get relevant help. Microsoft advises designers to consider whether the bot can solve the problem with minimal turns and guide users who need help, including through a live-agent handoff where appropriate. See Microsoft’s conversation design principles.
6. Keep the implementation manageable
As a flow grows, divide it into related pieces rather than making one sprawling diagram. Microsoft’s dialog model allows dialogs to call other dialogs and return control when they finish. In Google Cloud Dialogflow CX, flows group related pages, while pages act as nodes in a state machine. These are platform-specific implementation concepts; you can design the conceptual flow without using either platform.
For the platform concepts, see Microsoft’s conversation-flow guidance and Google Cloud’s agent design documentation.
7. Test scenarios and revise
Walk through the main route and plausible alternatives, then update both the dialogue and diagram when you find a confusing turn or missing branch. Test different personas and use cases, alternative wording, out-of-sequence replies, misunderstood input, and abandonment followed by resumption. Google recommends drafting for different personas and use cases; Google Cloud describes iterative agent design and test cases in its agent design documentation.
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Example: rescheduling an appointment with a side question
The following is an original illustrative exchange, not a transcript from a tested bot. It shows how a flow can preserve the pending task while handling a detour.
| Turn | Exchange | Flow decision |
|---|---|---|
| 1 | User: “I need to change my appointment.” Bot: “I can help with that. Which appointment do you mean: Tuesday’s visit or Friday’s call?” |
Identify the appointment. If no booking matches, explain that and offer a useful next step. |
| 2 | User: “Tuesday. Also, where do I park?” Bot: “I can help with parking too. The clinic address is [clinic address]. Would you like to continue changing Tuesday’s appointment?” |
Answer the side question, retain the selected appointment, and ask whether to resume. |
| 3 | User: “Yes.” Bot: “What day works better?” |
Resume the pending task and request a new preference. |
| 4 | User: “Thursday afternoon.” Bot: “I found Thursday at 2:30 p.m. Should I move the appointment to that time?” |
Check availability and confirm the specific change before committing it. |
| 5 | User: “Yes.” Bot: “Done. Your appointment is now Thursday at 2:30 p.m.” |
Complete the change and state the outcome. |
The diagram for this exchange should include appointment selection, the parking detour, return to the pending task, availability lookup, confirmation, and completion. Add branches for no matching appointment, no available time, an unclear date, cancellation, and a request for human help. Microsoft’s and IBM’s guidance supports considering interruptions and recovery; this example does not claim any particular product can perform these actions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Guided versus flexible flows
A mostly sequential flow can make the next step obvious, especially when the task requires information in a planned order. A more flexible flow can accept shortcuts, interruptions, or out-of-order information, but it needs logic that preserves enough context to resume without confusing the user. Neither approach is universally better. Compare the designs on these practical dimensions:
| Design question | What to assess |
|---|---|
| Task clarity | Does the user understand what to do next? |
| Efficiency | Can the user reach the goal without unnecessary back-and-forth? |
| Flexibility | Can the user skip ahead, change the subject, or provide information out of order? |
| Recovery | Can the bot clarify, explain a failure, resume the task, or offer help? |
| State and implementation complexity | Can the system retain the information needed to continue without losing track of the user’s goal? |
Microsoft recommends minimizing unnecessary turns while guiding users who are stuck; Google’s guidance emphasizes both defined routes and supported natural-language shortcuts. Use the dimensions above to decide what fits the task rather than assuming one flow style is always preferable.
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Common flow-design mistakes to avoid
- Designing only the happy path: A diagram without unclear input, missing information, detours, or failure outcomes leaves the bot with no designed response when the conversation departs from the script.
- Writing prompts without role-playing: A prompt that looks clear on a diagram may sound awkward or fail to tell the user what to do. Read or listen to every line in its intended mode.
- Treating the diagram as the dialogue: Boxes and arrows describe logic, not whether the actual exchange feels natural. Keep the sample dialogue and diagram in conversation as you revise.
- Forgetting the pending task during a detour: If the bot answers a side question, decide explicitly whether and how it will return to the task already in progress.
- Adding flexibility without state: Shortcuts and out-of-order replies are only helpful when the flow can track what has already been provided and what remains to be done.
- Leaving users stuck: Provide an appropriate clarification, help route, restart, or human handoff rather than an unexplained dead end.
Frequently Asked Questions
What is the difference between a chatbot script and a conversation flow?
A script or sample dialogue shows the actual words exchanged. A conversation flow maps the logic behind those words: routes, decisions, branches, and transitions. Both are useful, and Google recommends iterating between them.
Should a chatbot force users to follow a fixed sequence?
Only when the task benefits from a planned order and the flow makes that order clear. If users may reasonably provide details out of order or interrupt with a related request, design supported shortcuts and a reliable way to resume.
What should a chatbot do when it does not understand?
Design a recovery path suited to the task: clarify what was unclear, explain what information is needed, return to a prior question, restart when appropriate, or offer human help. Avoid leaving the user at a dead end.
Can I design a chatbot conversation flow without a specific platform?
Yes. Start with the user’s goal, sample dialogue, and a diagram of routes and branches. Platform constructs such as Microsoft dialogs or Dialogflow CX flows and pages are ways to implement conversation logic, not prerequisites for designing it.
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