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A good chatbot helps people finish a clearly defined task with less effort than the alternatives. It explains what it can do, handles mistakes without trapping users, protects their information, and offers a clear route to a person when automation is not enough. These practices apply to scripted and LLM-backed chatbots; voice-specific implementation advice is identified separately.
Start with the user’s task, not the chatbot
Choose a chatbot only when it can improve a real user experience. A chat interface is not automatically better than search, a form, documentation, or a human agent. Identify the task people are trying to complete, then compare whether a bot can help them do it more easily and reliably.
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Define a narrow, useful scope
Begin with a small set of high-value tasks. State what the bot can do and where its boundaries are. A bot designed to answer questions about order status, for example, should not imply that it can handle every possible customer concern. An open-ended promise invites requests the system cannot resolve and can lead users to trust it inappropriately.
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Check whether chat is the right channel
Compare the proposed bot with the existing way to complete the same task. If a short form or a clear help page is faster, a chatbot may add friction rather than remove it. Consider the full experience: how users discover the bot, when it appears, which platforms they use, and whether they can complete the task without repeating details or navigating unnecessary turns.
Design a conversation that takes less effort
Set expectations at the start
Tell users plainly that they are interacting with a bot and describe its useful capabilities. Avoid implying open-ended competence. Microsoft’s Human-AI Interaction Toolkit groups guidance around the initial experience, interaction in progress, what happens when AI is wrong, and changes in behavior over time. The toolkit currently describes 18 evidence-based guidelines; that number refers to the guidelines, not to a chatbot performance result.
Ask only for what is needed
Keep prompts short and straightforward. Ask for information only when it is needed to make progress, and use context that is legitimately available rather than asking people to repeat themselves. A bot should not turn a simple request into a long interview. If users have already supplied a relevant detail in the conversation, do not ask for it again without a clear reason.
Make common controls easy to use
Recognize ordinary phrases such as “help,” “settings,” “start over,” and “stop.” Account for likely misspellings and variations in how people describe the same intent. Give users a way to correct the bot, revise an answer, or regain control instead of forcing them to restart the interaction.
Plan for uncertainty, errors, and handoff
Respond usefully when the bot does not know
When a request is ambiguous, unsupported, or outside the bot’s scope, say so plainly. Do not invent an answer or present an uncertain response as established fact. Offer a sensible next action: ask one focused clarification question, point to a relevant resource, or route the user to a person.
Design recovery for more than unknown questions. Include misspellings, misunderstood intent, repeated prompts, failed actions, and abusive inputs in the interaction plan. Acknowledge a failed action and explain what the user can do next. Never claim that an action or transfer succeeded unless the underlying system confirms it.
Define the human route before launch
Decide which intents must go to a person, what the bot should say before transfer, what conversation context it may pass along, and what happens when no person is available. Offer human help at appropriate points and make the route easy to find. Repeatedly asking users to rephrase a request is not a substitute for escalation.
For sensitive or consequential situations, decide explicitly whether the bot should stop, provide limited assistance, or hand over. The appropriate boundary depends on the task and the consequences of an error; a general-purpose chatbot policy cannot settle every case.
Build trust, privacy, security, and inclusion into the experience
Use responsible-AI principles as design checks
Microsoft’s agent design foundations identify six principles to apply across an agent’s lifecycle: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These are principles for teams to apply and evaluate; their presence in a design document does not establish that a particular chatbot conforms to them.
Disclose the bot’s role and relevant limitations. Use authentication appropriate to the task, minimize exposure of sensitive information, and make clear when a user is interacting with AI. Do not collect details merely because the interface makes it easy to ask for them.
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Make accessibility part of conversation design
Consider both the interface and the bot’s language. A visual chat widget alone does not establish an accessible experience. Test the interaction with people with disabilities, including whether users can understand prompts, operate controls, recover from errors, and reach support through the channels provided.
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For systems using large language models, consider prompt injection, hallucinated or otherwise unsupported answers, data exposure, and unauthorized access. NIST’s July 2025 initial public draft, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations for Artificial Intelligence Systems (NIST IR 8579), documents a prototype and related security learnings, including access controls and validation filters. NIST describes that report as a point-in-time examination, not implementation guidance or a universal security standard. Teams still need to assess their own architecture, data, permissions, and risks.
Test complete tasks and failure cases
Build a scenario matrix
Test representative interactions before release and after material changes. A useful scenario matrix covers ordinary tasks as well as cases where the system is likely to struggle:
- Common user tasks and variations in wording.
- Ambiguous requests, misspellings, and missing information.
- Questions outside the bot’s scope and cases where it has no reliable answer.
- Tool or action failures, including whether the user receives an accurate status and a recovery option.
- Requests that should go to a person, including the handoff and the no-agent-available path.
- LLM-specific risks such as prompt injection, data exposure, unsupported answers, and unauthorized access.
- Accessibility needs and use of the bot across the platforms it supports.
This matrix is a practical way to operationalize UX and security guidance, not a published benchmark. A successful demonstration of one expected conversation does not show how the bot behaves under ambiguity or failure.
Measure effort as well as answer quality
Assess whether users complete the task and how much effort it takes: turns required, repeated details, extra steps, abandonment, and requests for a person are all useful signals. Review failed and incomplete interactions to determine whether the right response is a narrower scope, better content, a changed conversation, or a different escalation rule.
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Reassess behavior after updates
Expectations and user control need attention over time, especially when an AI system changes. Revisit representative scenarios after updates, review new failure patterns, and make sure users can still understand the bot’s capabilities and correct its mistakes. For releases that could make the system riskier or less reliable, have a tested way to restore a prior version.
Voice-agent practices that do not automatically apply to text chat
Voice interfaces add constraints that text-chat teams should not assume apply to their own implementation. Microsoft’s voice-based agent guidance addresses latency, turn-taking, interruptions, speech-recognition failures, and spoken recovery. It recommends handling failures out loud, testing across channels, and retaining a tested rollback version. These are voice-agent implementation considerations, not universal requirements for every text chatbot.
Common chatbot mistakes and how to avoid them
| Common mistake | Why it causes problems | Better practice |
|---|---|---|
| Starting with the technology instead of a user task | The bot may add a step without improving the existing experience. | Compare it with search, a form, documentation, or human support for a defined task. |
| Promising broad competence | Users may ask for unsupported help or trust answers beyond the system’s capability. | Describe a narrow scope and make its boundaries understandable. |
| Making people repeat details or endure unnecessary turns | The conversation increases effort instead of reducing it. | Ask only for information needed to proceed and use legitimate context already provided. |
| Treating handoff as an afterthought | Users can become stuck when they need judgment or the bot cannot resolve the issue. | Define transfer triggers, context sharing, the pre-transfer message, and the unavailable-agent path. |
| Ignoring errors and unsupported requests | Misspellings, ambiguity, and failed actions are ordinary interaction conditions. | Provide clear recovery, clarification, resource, or human-help options. |
| Testing only the happy path | Important UX and security failures may appear only under ambiguity, tool failure, or adversarial input. | Test a scenario matrix that includes edge cases, handoff, and recovery. |
| Assuming AI is automatically trustworthy or inclusive | Trust, security, privacy, accessibility, fairness, and accountability require deliberate work. | Build those checks into design, testing, and ongoing review. |
| Treating a draft security report as a standard | A prototype report does not prescribe implementation for every chatbot. | Use NIST IR 8579 as a point-in-time account of a prototype, not as implementation guidance. |
How to choose or improve a chatbot approach
Use the same questions whether deciding between a chatbot and another channel or comparing two chatbot designs:
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- Task fit: Can this approach resolve the intended need, and which cases belong with a person?
- Effort: How many turns, repeated details, and extra actions does completion require?
- Capability clarity: Can users understand what the bot can do and how reliable it is for this task?
- Failure recovery: Does it admit uncertainty, recover from mistakes, and offer human help?
- Trust and inclusion: Are privacy, security, accessibility, fairness, transparency, and user control addressed?
- Evaluation and operations: Can the team test realistic scenarios, review failures, monitor changes, and recover from a risky release?
A chatbot is a good fit when it helps users complete a bounded task with less friction while preserving clear expectations and a usable path out when automation falls short. If it cannot do that, a simpler channel or direct human support may serve the user better.
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Frequently Asked Questions
How do I design a chatbot?
Begin with a specific user task, define the bot’s boundaries, and design the shortest reliable path to completion. Include clear expectations, correction and recovery options, and a human route where needed; then test ordinary and failure scenarios.
What should a chatbot do when it doesn’t know the answer?
It should acknowledge the uncertainty rather than guess, then offer an appropriate next step such as a focused clarification, a relevant resource, or a person.
How can users get help from a human?
Give users an easy-to-find human-help option and define when the bot should offer or initiate a handoff. Explain what happens if no person is available and pass along only appropriate conversation context.
Is there a universal chatbot success rate to target?
No universal accuracy, satisfaction, or task-completion percentage is established by the cited guidance. Evaluate outcomes and effort for the specific task and users instead of treating an unsupported generic target as a benchmark.
Frequently Asked Questions
How do I design a chatbot?
Begin with a specific user task, define the bot’s boundaries, and design the shortest reliable path to completion. Include clear expectations, correction and recovery options, and a human route where needed; then test ordinary and failure scenarios.
What should a chatbot do when it doesn’t know the answer?
It should acknowledge the uncertainty rather than guess, then offer an appropriate next step such as a focused clarification, a relevant resource, or a person.
How can users get help from a human?
Give users an easy-to-find human-help option and define when the bot should offer or initiate a handoff. Explain what happens if no person is available and pass along only appropriate conversation context.
Is there a universal chatbot success rate to target?
No universal accuracy, satisfaction, or task-completion percentage is established by the cited guidance. Evaluate outcomes and effort for the specific task and users instead of treating an unsupported generic target as a benchmark.
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