Prompt suggestions can range in complexity from short phrases to full conversations, images, or videos that illustrate a complete interaction. The level of detail depends on factors such as the system’s capabilities, user familiarity with AI, task complexity, and placement within the interface.

• Simple prompts—for example, short clickable phrases—work well for broad systems and low‑complexity tasks.
• Complex prompts—such as cards, carousels, or videos—are more suitable for specialized systems and high‑complexity tasks.

Placement also matters. Compact prompts near the input field encourage quick engagement, while richer examples may be displayed one at a time through cards or carousels.

Impact on New Users
Before users sign in, many AI products use curated prompt suggestions to highlight core capabilities and encourage registration. These prompts are often displayed as pills or cards and emphasize features such as content creation, problem‑solving, or ideation.
Examples include:

• ChatGPT: simple pill suggestions like “Analyze data” or “Summarize text.”
• Poe.com: card‑based suggestions showing more detailed interactions.
• Notion AI: a pre‑login video demonstrating capabilities.
• Claude: a carousel of example conversations highlighting tasks such as visualizing data or optimizing code, though simplified for quick comprehension.

These preauthentication prompts are intended to be quick to scan, visually unobtrusive, and clearly tied to common goals, avoiding usability issues such as intrusiveness or irrelevance.

Impact on Active Users
For authenticated users, prompt suggestions shift toward supporting ongoing workflows and inspiring deeper use. Context‑aware suggestions adapt to the user’s current task or knowledge level, offering just‑in‑time guidance.
Examples include:

• Tripadvisor: surfacing travel questions like “Which hotels in Miami have ocean views?”
• Amazon’s Rufus AI: tailoring product‑specific suggestions such as “Do Birkenstocks come in both narrow and wide?”
• Instacart: providing targeted search prompts like “Nutritious snacks for kids,” which users reported as immediately helpful.

Systems can also individualize prompts based on prior activity, user expertise, or patterns from other users when personal data is unavailable.

Supporting Design Choices
Effective prompt suggestions are often placed near the input field to maximize visibility at the moment of interaction. Example libraries with curated input‑output pairs can provide further education and inspiration, especially in visual AI tools like Midjourney. To maintain quality, product teams typically review and select examples rather than relying solely on automated outputs.

Analytics are essential for refining prompt strategies. Data can reveal common use cases and show which suggestions drive engagement. Because placement influences click rates, teams may randomize display order to measure true content effectiveness.

Conclusion
Use‑case prompt suggestions serve as a key onboarding and engagement tool for AI systems. Simple prompts help new users quickly grasp capabilities, while context‑aware, tailored suggestions guide active users through more complex workflows. By carefully balancing complexity, placement, and content—and continuously refining through analytics—designers can make prompt suggestions both informative and actionable, improving usability and adoption over time.