Building AI features that don’t surprise your users

<h2>Make the AI’s role explicit</h2><p>Define whether the AI is suggesting, drafting, classifying, or deciding.</p><p>Users should know what’s automated and what needs review.</p><h2>Use confidence thresholds and fallbacks</h2><p>When confidence is low, route to a safe default: manual review or a standard workflow.</p><p>Log decisions and measure accuracy over time.</p><h2>Design for auditability</h2><p>Store inputs, outputs, versions, and user overrides.</p><p>This reduces compliance risk and speeds debugging.</p><h2>FAQs</h2><h3>Do we need to train our own model?</h3><p>Often no. Start with APIs and focus on product value. Train later if it’s cost-effective or needed for privacy.</p><h3>How do we measure success?</h3><p>Use metrics tied to user outcomes: time saved, error reduction, conversion, or support load.</p><h3>How do we handle hallucinations?</h3><p>Limit the AI’s scope, use retrieval for factual answers, and provide safe disclaimers and fallbacks.</p><h2>Next step</h2><p>If you want help applying this to your product, contact Webokit or book a call.</p><ul><li><a href=\"/services/ai-ml-development\">AI/ML Development service</a></li><li><a href=\"/contact\">Contact</a></li><li><a href=\"/process\">Process</a></li></ul>