Users Stare at Your AI Feature and Leave: Fixing the Blank Prompt Problem
You built the AI feature. Users click in, see an empty text box, and leave. 60% of users never send a first message. Here's how to fix the blank prompt problem with better empty states.
November 2, 2025 5 min read
You shipped your AI feature. It works beautifully. The responses are accurate, fast, and helpful.
Nobody uses it.
Analytics show 60% of users who open the feature never send a message. They stare at the empty prompt box. They don't know what to ask. They leave. This is one of the most frustrating problems we solve in AI development projects.
The blank prompt problem kills more AI features than bad responses ever will.
Why Empty Prompt Boxes Fail
A blank text input is the highest-friction interface you can build.
What users think when they see an empty prompt:
"What should I ask?"
"What is this even for?"
"What kind of questions work here?"
"What if I ask the wrong thing?"
The cognitive load:
Formulate a question (what do I want to know?)
Phrase it correctly (how do I ask this?)
Assess if it's appropriate (will this work?)
Overcome fear of looking stupid (what if it doesn't understand?)
That's four friction points before the user types a single character.
Contrast with traditional UI:
Buttons tell you exactly what they do. Forms have labeled fields. Dropdowns show available options. Users know what actions are possible.
Stop planning and start building. We turn your idea into a production-ready product in 6-8 weeks.
AI prompt boxes offer infinite possibilities and zero guidance.
The Empty State That Actually Works
Your empty state should answer three questions:
What is this feature for?
What can I do with it?
How do I get started?
Bad empty state:
Zero information. Maximum anxiety.
Good empty state:
Now users know exactly what this feature does and have concrete examples to start with.
Conversation Starters: Clickable Examples
Don't make users type. Give them buttons to click.
Implementation pattern:
Why this works:
Zero typing required to start
Clear examples of what's possible
Categorized by use case (helps discovery)
Custom input still available for power users
Engagement impact:
Without starters: 40% of users submit first prompt
With starters: 78% of users engage (source: internal data from projects)
Context-Aware Suggestions: Show What's Relevant
Generic examples are better than nothing. Context-specific examples are better than generic.
Pattern: Suggest based on current context
Display pattern:
Users see prompts directly relevant to what they're doing right now. Conversion to first message increases dramatically.
Prompt Templates: Fill-in-the-Blanks
Some tasks need specific information. Don't make users guess the format.
Bad UX:
Better UX: Structured templates
Benefits:
Users know exactly what information to provide
AI gets well-structured prompts (better outputs)
No back-and-forth clarification needed
Clear expectations set upfront
When to use templates:
Document generation (emails, reports, summaries)
Data analysis (need specific parameters)
Content transformation (translation, rewriting)
Complex queries with multiple requirements
Progressive Disclosure: Guide Users Step-by-Step
For complex AI features, don't overwhelm with options. Guide users through a flow.
Pattern: Multi-step prompt builder
Why this works:
One decision at a time (lower cognitive load)
Options reveal progressively (not overwhelming)
Clear path to completion (users see progress)
Can back up and change selections
Examples from Your Data: Show What's Possible
Generic examples are okay. Examples using the user's actual data are powerful.
Pattern: Generate suggestions from user's context
Display:
Users see concrete examples of what your AI can do with their specific data. Much more compelling than generic "Analyze your data" prompts.
Placeholder Text That Guides
If you're keeping a text input, make the placeholder text work harder.
Bad placeholders:
Better placeholders:
Dynamic placeholders:
Dynamic placeholders show multiple examples without cluttering UI.
Onboarding Flow: First-Time User Experience
First-time users need extra guidance.
Pattern: Interactive tutorial
Onboarding metrics to track:
Completion rate (% who finish tutorial)
Time to first query
First query success rate (did they get useful response?)
Return rate after first session
Onboarding investment pays off. Users who complete tutorials are 3x more likely to become regular users.
For startups, this kind of onboarding can be the difference between viral growth and silent failure.
The 5-Second Rule
Users decide whether to engage with your AI feature in under 5 seconds.
What they need to see in those 5 seconds:
What this does (clear heading, not "AI Assistant")
How to use it (visible examples or buttons)
That it's easy (low-friction starting point)
Audit your empty state:
Open your AI feature in incognito mode (fresh eyes)
Start a 5-second timer
Can you answer: What is this for? What can I do? How do I start?
If not, your empty state needs work.
Real Metrics: What Good Empty States Achieve
Before optimization (typical AI feature):
60% of users never send first message
85% of users who start don't return
Average time to first prompt: 45 seconds
After optimization (with good empty state):
78% of users send first message
65% of users return within 7 days
Average time to first prompt: 8 seconds
The difference between a blank text box and a well-designed empty state is 3x engagement.
We see this in every project where we focus on UX. The AI quality barely matters if users never start.
Building This: Implementation Checklist
Week 1: Basic empty state
Replace generic placeholder with specific examples
Add 3-5 clickable conversation starters
Group by category (Popular, Analysis, Writing, etc.)
Week 2: Context awareness
Detect user context (uploaded docs, viewing data, etc.)
Generate contextual suggestions
Show personalized examples using user's data
Week 3: Templates and progressive disclosure
Build 2-3 prompt templates for common tasks
Implement progressive prompt builder for complex queries
Add step indicators and back buttons
Week 4: Onboarding and polish
Create first-time user tutorial
Add dynamic placeholders
Track metrics and iterate
This is not optional polish. It's the difference between an AI feature that gets used and one that gets ignored.
The Empty State Is Your Real Landing Page
You optimized your marketing site. You A/B tested your signup flow. You nailed your onboarding emails.
Then users click into your AI feature and see an empty text box.
That empty state is your real landing page. It's where users decide if your AI feature is worth their time.
Blank prompts lose 60% of users before they start. Good empty states convert 78%. The difference is worth a week of design work.
If you're building an MVP, investing in empty state design from the start will dramatically improve your activation metrics.
Ready to build AI features that users actually engage with? Talk to our team about UX-first AI implementation, or calculate your MVP timeline to see how quickly we can ship this.
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┌─────────────────────────────────────┐│ Ask me anything... │└─────────────────────────────────────┘
text
Get instant answers about your customer dataTry asking:• "What were our top products last month?"• "Show me customer retention by segment"• "Which marketing channels drive the most revenue?"Or describe what you want to know in your own words.┌─────────────────────────────────────┐│ Ask about your data... │└─────────────────────────────────────┘
jsx
function AIPromptInterface() { const [input, setInput] = useState(""); const [hasStarted, setHasStarted] = useState(false); const starters = [ { category: "Popular", prompts: ["Summarize this document", "Translate to Spanish", "Extract key points"], }, { category: "Analysis", prompts: [ "What are the main themes?", "Compare this to industry standards", "Identify risks and opportunities", ], }, { category: "Writing", prompts: [ "Write a professional email", "Create a bullet-point summary", "Generate FAQ from this content", ], }, ]; if (hasStarted) { return <ChatInterface input={input} />; } return ( <div className="empty-state"> <h2>What can I help you with?</h2> {starters.map((category) => ( <div key={category.category}> <h3>{category.category}</h3> <div className="prompt-buttons"> {category.prompts.map((prompt) => ( <button key={prompt} onClick={() => { setInput(prompt); setHasStarted(true); submitPrompt(prompt); }} > {prompt} </button> ))} </div> </div> ))} <div className="custom-prompt"> <input placeholder="Or type your own question..." value={input} onChange={(e) => setInput(e.target.value)} onKeyDown={(e) => { if (e.key === "Enter") { setHasStarted(true); submitPrompt(input); } }} /> </div> </div> );}
javascript
function getContextualPrompts(context) { const prompts = []; // If user uploaded a document if (context.hasDocument) { prompts.push({ text: `Summarize ${context.documentName}`, icon: "📄", }); prompts.push({ text: `Extract key points from ${context.documentName}`, icon: "🔑", }); } // If user has selected text if (context.selectedText) { prompts.push({ text: "Explain this selection", icon: "💡", }); prompts.push({ text: "Translate this text", icon: "🌍", }); } // If user is looking at data table if (context.viewingData) { prompts.push({ text: `Analyze trends in ${context.dataSource}`, icon: "📊", }); prompts.push({ text: "Find anomalies in this data", icon: "🔍", }); } // If user just finished an action if (context.lastAction === "created_report") { prompts.push({ text: "Generate an executive summary of this report", icon: "📝", }); } // Always include popular defaults prompts.push(...getPopularPrompts()); return prompts.slice(0, 6); // Max 6 suggestions}
┌─────────────────────────────────────┐│ Ask me anything... │└─────────────────────────────────────┘User types: "write email"AI: "I need more information. What should the email be about?"User: "product launch"AI: "What tone should I use?"User: (gives up)
jsx
function EmailTemplate() { const [purpose, setPurpose] = useState(""); const [tone, setTone] = useState("professional"); const [keyPoints, setKeyPoints] = useState(""); return ( <div className="prompt-template"> <h3>Write an email</h3> <label> Purpose: <input placeholder="e.g., Announce new product launch" value={purpose} onChange={(e) => setPurpose(e.target.value)} /> </label> <label> Tone: <select value={tone} onChange={(e) => setTone(e.target.value)}> <option value="professional">Professional</option> <option value="casual">Casual</option> <option value="formal">Formal</option> <option value="friendly">Friendly</option> </select> </label> <label> Key points to include: <textarea placeholder="• Point 1 • Point 2 • Point 3" value={keyPoints} onChange={(e) => setKeyPoints(e.target.value)} /> </label> <button onClick={() => { const prompt = `Write a ${tone} email about: ${purpose}Include these key points:${keyPoints}Keep it concise and include a clear call to action.`; submitPrompt(prompt); }} > Generate Email </button> </div> );}
async function generatePersonalizedExamples(userId) { const userContext = await getUserContext(userId); const examples = []; // If user has recent activity if (userContext.recentOrders?.length > 0) { examples.push(`Analyze trends in my orders from the last 30 days`); } // If user has uploaded documents if (userContext.documents?.length > 0) { const recentDoc = userContext.documents[0]; examples.push(`Summarize key points from "${recentDoc.name}"`); } // If user has team members if (userContext.teamSize > 1) { examples.push(`Compare performance across my ${userContext.teamSize} team members`); } // If user has projects if (userContext.projects?.length > 0) { examples.push(`What are the blockers in my active projects?`); } return examples;}
jsx
<div className="personalized-examples"> <h4>Try these with your data:</h4> {personalizedExamples.map((example) => ( <button key={example} onClick={() => submitPrompt(example)}> {example} </button> ))}</div>
text
Ask me anything...Type your question here...What do you want to know?
text
Try: "Summarize this document" or "Translate to Spanish"Ask about your sales data, customer trends, or product performancee.g., "What were our top products last quarter?" or "Show customer retention by segment"
javascript
const placeholders = [ "Summarize this document in 3 key points", "What are the main themes in this content?", "Translate this to Spanish", "Extract action items from this text", "Write a professional summary",];// Rotate every 3 secondsuseEffect(() => { const interval = setInterval(() => { setPlaceholder(placeholders[Math.floor(Math.random() * placeholders.length)]); }, 3000); return () => clearInterval(interval);}, []);
jsx
function AIFeatureOnboarding() { const [step, setStep] = useState(1); const tutorial = [ { title: "Welcome to AI Assistant", description: "Ask questions about your data in plain English", action: "Get Started", }, { title: "Try Your First Query", description: "Click any example below to see how it works", examples: [ "What were my top products last month?", "Show me customer trends", "Analyze my sales data", ], }, { title: "Ask Your Own Questions", description: "Now try asking something specific to your needs", freeform: true, }, ]; if (step > tutorial.length) { return <MainInterface />; } return ( <div className="onboarding"> <h2>{tutorial[step - 1].title}</h2> <p>{tutorial[step - 1].description}</p> {tutorial[step - 1].examples && ( <div className="examples"> {tutorial[step - 1].examples.map((example) => ( <button key={example} onClick={() => { submitPrompt(example); setStep(step + 1); }} > {example} </button> ))} </div> )} {tutorial[step - 1].freeform && ( <input placeholder="Ask your own question..." onKeyDown={(e) => { if (e.key === "Enter" && e.target.value) { submitPrompt(e.target.value); setStep(step + 1); } }} /> )} <button onClick={() => setStep(step + 1)}> {tutorial[step - 1].action || "Next"} </button> </div> );}
