Page-level AI features enhance entire pages rather than individual components. This includes page assistants that help users navigate and understand your application, smart search that understands natural language queries, and contextual help systems. This article explores these powerful page-wide AI capabilities.

Page assistants are AI helpers that users can interact with on any page. They understand the context of the current page and can answer questions, provide guidance, or perform actions based on what the user is viewing.

Step 1: Enable Page Assistant

1. Go to Page Settings
2. Enable "AI Assistant"
3. Choose assistant appearance (floating button, sidebar, embedded)

Step 2: Configure Context

1. Define what data assistant can access
2. Set page context and purpose
3. Configure available actions

Step 3: Write System Prompt

Define assistant behavior and capabilities.

Scenario: Executive dashboard with AI assistant to explain metrics.

System Prompt:

You are a helpful business intelligence assistant. You can see the following dashboard data:

- Total Revenue: ${Total Revenue}
- Revenue Growth: {Growth Percentage}%
- New Customers: {New Customer Count}
- Customer Churn: {Churn Percentage}%
- Top Products: {Top 5 Products}
- Regional Performance: {Region Data}

Answer questions about this data, provide insights, and explain trends. Keep responses concise and business-focused. If asked about data not on this dashboard, say you can only see current dashboard metrics.

Example Interactions:

• User: "Why did revenue grow this month?"
• AI: "Revenue grew 15% primarily due to increased sales in the Electronics category ($45K) and strong performance in the West region (+22%)."

• User: "Should I be concerned about the churn rate?"
• AI: "The 3.2% churn rate is slightly above your target of 3%. Consider focusing on customer retention in segments with highest churn, particularly accounts under $1000 monthly value."

Scenario: Complex form with AI assistant to help users complete it.

System Prompt:

You are a helpful form assistant. You're helping users complete a project proposal form.

The form includes:
- Project Title
- Description
- Budget Requirements
- Timeline
- Team Members
- Success Metrics

Help users by:
- Explaining what information is needed for each field
- Providing examples when asked
- Suggesting content when users are stuck
- Validating that required information is included
- Answering questions about submission requirements

Current form state:
{Current Field Values}

Be encouraging and helpful. Keep responses brief.

Example Interactions:

• User: "What should I put for success metrics?"
• AI: "Success metrics should be specific, measurable goals. For example: 'Reduce processing time by 30%' or 'Increase customer satisfaction score to 4.5/5'. What outcomes are you hoping to achieve?"

Scenario: Data table with assistant for natural language querying.

System Prompt:

You are a data assistant helping users explore customer data. You can see:

{Visible Records on Page}

Total Records: {Total Record Count}
Applied Filters: {Current Filters}

Help users by:
- Answering questions about visible data
- Suggesting useful filters or views
- Identifying patterns and trends
- Explaining outliers
- Recommending actions based on data

Be analytical but conversational.

Example Interactions:

• User: "Show me customers at risk of churning"
• AI: "I'll help you find at-risk customers. Try filtering for: No orders in 90+ days, Support tickets > 3, and Customer satisfaction

Smart search uses AI to understand natural language queries and find relevant records, even when the search terms don't exactly match your data. It interprets intent, handles synonyms, and provides contextually relevant results.

Traditional Search:

• User types: "urgent orders"
• Searches for exact text "urgent orders"
• Might miss orders marked "high priority" or "rush"

Smart Search:

• User types: "urgent orders"
• AI understands: looking for time-sensitive orders
• Finds: Priority="High", Rush="Yes", Requested Date=Soon
• Returns: All urgent orders regardless of exact wording

Step 1: Enable Smart Search

1. Go to Page Settings
2. Find Search Configuration
3. Enable "AI-Powered Search"

Step 2: Configure Search Scope

1. Select which fields AI can search
2. Define search context
3. Set result formatting

Step 3: Define Semantic Context

This search covers customer records with these fields:
- Company Name
- Industry
- Status (Active, Inactive, Pending)
- Revenue (annual)
- Last Order Date
- Priority (High, Medium, Low)

Understand queries like:
- "big customers" = Revenue > $100k
- "inactive" = No orders in 90+ days or Status = Inactive
- "tech companies" = Industry contains technology-related terms
- "VIP" or "important" = Priority = High or Revenue > $250k

Return relevant results even if exact terms don't match.

Query: "Show me customers we haven't contacted recently"

AI Interpretation: Looking for customers with no recent interactions

Filter Applied: Last Contact Date > 60 days ago

Results: Displays customers matching criteria with last contact date highlighted

Query: "Healthcare clients in the Northeast"

AI Interpretation: Looking for industry="Healthcare" AND region="Northeast"

Also Finds: Industry="Medical", "Hospital", "Healthcare Services", etc.

Region: NY, NJ, MA, CT, etc.

Query: "Problematic orders this week"

AI Interpretation: Issues with recent orders

Looks For: Status="Problem", "Delayed", "Error" OR Support Tickets > 0

Timeframe: This week

Contextual help provides assistance relevant to what the user is currently doing. Instead of generic help documentation, AI delivers specific guidance based on the current page, task, or data.

Scenario: New user onboarding with AI-guided help.

Implementation:

You are an onboarding assistant helping new users learn the system.

Current Page: {Page Name}
User Progress: {Completed Steps}
User Role: {User Role}

Provide:
- Brief explanation of current page purpose
- What user can do here
- Suggested first actions
- Tips relevant to their role
- Links to related pages

Adapt help to user's experience level. Be encouraging and concise.

Dynamic Help Content:

• First Visit: Detailed explanation and walkthrough
• Return Visits: Quick tips and shortcuts
• Advanced Use: Power user features and optimization

Page-level AI prompts allow users to interact with all data on a page simultaneously. Users can ask questions, request analysis, or generate insights based on everything visible.

Scenario: Sales page showing pipeline of opportunities.

Page Context:

Visible Data: {List of Opportunities with Status, Value, Close Date, Owner}

Total Pipeline Value: ${Pipeline Total}
Number of Opportunities: {Count}
Average Deal Size: ${Average}
Current Filters: {Active Filters}

Sample Questions:

"Which deals are most likely to close this month?"

AI Analyzes: Close dates, stages, deal sizes, historical patterns

Response: Provides list with reasoning

"Why is the pipeline smaller than last month?"

AI Analyzes: Current vs historical data

Response: "Pipeline value is down 15% primarily because 3 large deals ($150K total) closed last month, and new deals added this month are smaller. You have 12 new opportunities but average $8K vs $15K last month."

AI assistant that remembers context across page navigation.

Example:

• User asks about customer on list page
• Navigates to customer detail page
• AI maintains conversation context
• Can refer back to previous pages

AI automatically surfaces important information without being asked.

Example:

• User opens dashboard
• AI notices significant change in metrics
• Proactively shows: "Revenue jumped 25% yesterday, primarily from Enterprise segment"

AI guides users through multi-step processes.

Example:

1. User starts complex task
2. AI explains each step
3. Validates completion before proceeding
4. Provides tips and warnings
5. Celebrates completion

AI suggests page layout and component changes based on usage.

Example:

AI notices: "You frequently filter for high-priority items. Would you like me to add a quick filter button?"

When implementing page-level AI:

Planning

• Identify pages that would benefit from AI
• Define what assistance users need
• Map out available context
• Plan assistant personality and tone
• Consider user roles and needs

Implementation

• Write clear system prompts
• Configure context correctly
• Set up appropriate triggers
• Design intuitive UI
• Test with real users

Optimization

• Monitor usage patterns
• Collect user feedback
• Refine prompts based on interactions
• Optimize performance
• Expand to more pages

You now understand page-level AI features. The next article covers AI prompt engineering - how to write effective prompts that produce consistent, high-quality results.

Hands-On Exercise (To Be Added)

Exercise placeholders will include practical activities such as:

• Setting up a page assistant for a dashboard
• Configuring smart search for natural language queries
• Creating contextual help for a complex form
• Testing different assistant personalities and tones

Knowledge Check (To Be Added)

Quiz questions will test understanding of:

• Differences between page assistants and component AI
• Best use cases for smart search
• Contextual help implementation strategies
• Privacy and security considerations