In the rapidly evolving landscape of generative AI, the most impactful products aren’t built by chasing the latest models—they're built by deeply understanding user needs. This post outlines 10 essential principles for designing AI features that are practical, transparent, and centered on real-world workflows. Whether you're working with the latest language models or exploring new multimodal systems, the focus should remain the same: creating human-centered tools that enhance trust, control, and usability—not just adding AI for its own sake.

1. Start with the Job, Not the Model

Before touching a single prompt or model API, ask:
What job is the user trying to get done?

• Don’t start with “how can we use GPT-4?”
• Start with: “How do users currently summarize user feedback? Where is the friction?”

AI is a means, not the mission. Solve a problem, not just “add AI.”

2. Ensure Transparency and Explainability

LLMs can sound smart even when they’re wrong. Trust is built through transparency.

• Let users see the source of generated content (e.g. show the sentences pulled from user feedback).
• Highlight keywords or data points that influenced a summary or recommendation.
• Make it easy to trace how a result was created.

If users don’t know how your AI works, they won’t trust or adopt it.

3. Design for Human Oversight

Even the best models hallucinate or misinterpret edge cases.

Design for human-in-the-loop workflows:

• Let users review, edit, and correct AI output.
• Use “suggest, don’t act” patterns.
• Consider lightweight approval systems before anything is published or saved.

Users should feel in control, not at the mercy of the model.

4. Design for Error Recovery

Generative AI will get things wrong. Plan for it.

• Offer an “Undo” or “Regenerate” button.
• Let users ask follow-up questions or provide clarifying input.
• Don’t force users to refresh or restart when the model gives a bad result.

The recovery path should be as smooth as the success path.

5. Don’t Overpromise

Avoid marketing your AI features like magic.

• Use humble, accurate language: “Generate draft,” not “Write the perfect pitch.”
• Be clear about what it can and can’t do.
• Let users know when the model is guessing or when confidence is low.

Set realistic expectations to avoid user disappointment and erosion of trust.

6. Make the AI Explain Itself

Users don’t just want results—they want understanding.

• Display why something was generated: “This topic was extracted based on keywords like ‘frustrated’ and ‘checkout flow’.”
• Tie AI outputs to inputs (documents, examples, metrics).
• Use natural language explanations or even simple tooltips.

This turns a black box into a glass box.

7. Respect Context and Personalization

Generic answers aren’t helpful. Use the user’s context.

• Tailor responses to their role, data, project, or history.
• Let them provide lightweight guidance (“This is for a beginner” or “Tone: friendly and concise”).
• Save preferences and learn from interaction patterns.

Contextualized AI is vastly more useful—and more human.

8. Provide Control Without Complexity

Users should feel they’re guiding the AI, not configuring it.

• Offer simple controls: sliders, checkboxes, dropdowns.
• Let users refine: “Shorter,” “More detailed,” “Add a comparison.”
• Avoid technical jargon like “temperature” or “top-p”—unless your users are developers.

The best interfaces feel like a conversation, not a settings panel.

9. Design the Full Workflow, Not Just the Feature

AI can’t exist in a vacuum. It has to fit into the user’s real tasks.

• Ask: After this output, what’s the next step?
• Can they export it? Share it? Connect it to Jira, Figma, Notion?
• Can the AI help with prioritization, synthesis, follow-up, not just generation?

Design the journey, not the endpoint.

10. Build for Learning and Feedback Loops

AI products should learn from use—and help users learn too.

• Let users give feedback on outputs (“Was this helpful?”).
• Improve results based on thumbs up/down or text edits.
• Educate users passively: show best practices, explain features, give examples.

The more your system (and users) learn, the better the outcomes.

In Summary

Generative AI isn’t a silver bullet. It’s a powerful—but unpredictable—tool that requires careful, human-centered design. The best AI products feel trustworthy, explainable, and useful, not magical or mysterious.