Skip to content

AI-Powered Schema Assistant - PRD

AI-Powered Schema Assistant - Product Requirements Document

Section titled “AI-Powered Schema Assistant - Product Requirements Document”

The AI-Powered Schema Assistant is a conversational AI agent integrated into the Convex development environment that helps developers design, migrate, and optimize database schemas through natural language interactions. This feature addresses the complexity barrier that slows schema evolution and makes Convex more accessible to developers of all experience levels.

As a full-stack developer, I want to describe my data needs in plain English so that I can quickly create optimized schemas without deep Convex expertise.

As a team lead, I want automated migration planning so that I can confidently make schema changes without risking data loss or downtime.

As a new Convex user, I want interactive guidance so that I can learn best practices while building my first application.

As a senior developer, I want performance optimization suggestions so that I can proactively improve query efficiency.

As a DevOps engineer, I want rollback-ready migrations so that I can safely deploy schema changes in production.

As a product manager, I want schema impact analysis so that I can understand the implications of feature requests.

  • Input: Natural language descriptions of data requirements
  • Processing: Parse intent, map to Convex schema patterns, generate code
  • Output: Valid Convex schema definitions with explanations
// Example interaction
User: "Create a user system with profiles and social connections"
Assistant: Generated schema with users, profiles, and connections tables
  • Analysis: Compare current and target schemas
  • Generation: Create step-by-step migration plans
  • Validation: Check for potential data loss or conflicts
  • Rollback: Generate reverse migration scripts
  • Visualization: Entity-relationship diagrams
  • Navigation: Click-to-explore table relationships
  • Insights: AI-generated optimization recommendations
  • Documentation: Auto-generated schema documentation
  • Monitoring: Analyze query patterns in development
  • Suggestions: Recommend indices, query improvements
  • Validation: Test performance impact of changes
  • Reporting: Performance improvement metrics
Terminal window
convex schema chat "Add user authentication system"
convex schema migrate --interactive
convex schema optimize --auto
  • VS Code extension with chat interface
  • IntelliSense integration for schema suggestions
  • Real-time validation and error highlighting
  • Schema assistant panel in web dashboard
  • Visual schema designer with AI guidance
  • Migration history and rollback interface
  • Language Model: Fine-tuned on Convex patterns and documentation
  • Context Management: Maintains conversation state and project context
  • Pattern Recognition: Identifies common schema design patterns
  • Code Generation: Produces valid Convex schema and migration code
  • Parser: Analyzes existing schema definitions
  • Dependency Tracker: Maps table relationships and query patterns
  • Impact Calculator: Predicts effects of schema changes
  • Performance Profiler: Identifies optimization opportunities
  • Plan Generator: Creates sequential migration steps
  • Validation Engine: Checks migration safety and data integrity
  • Rollback Generator: Creates reverse migration scripts
  • Execution Monitor: Tracks migration progress and handles errors
  • CLI Interface: Command-line tools for development workflow
  • IDE Extensions: Editor integrations for real-time assistance
  • Dashboard API: Web interface for visual schema management
  • Webhook System: Notifications and external tool integration
interface ConversationContext {
projectId: string;
currentSchema: SchemaDefinition;
conversationHistory: Message[];
userPreferences: UserPreferences;
activeSession: SessionMetadata;
}
interface SchemaAnalysis {
tables: TableAnalysis[];
relationships: RelationshipMap;
queryPatterns: QueryPattern[];
performanceMetrics: PerformanceData;
optimizationSuggestions: Suggestion[];
}
interface MigrationPlan {
steps: MigrationStep[];
rollbackPlan: RollbackStep[];
riskAssessment: RiskAnalysis;
estimatedDuration: Duration;
prerequisites: Prerequisite[];
}
POST /api/schema/chat
{
message: string;
context: ConversationContext;
projectId: string;
}
Response: {
reply: string;
codeGenerated?: string;
suggestions: Suggestion[];
followUpQuestions: string[];
}
POST /api/schema/migrate
{
targetSchema: SchemaDefinition;
options: MigrationOptions;
}
Response: {
plan: MigrationPlan;
estimatedRisk: RiskLevel;
rollbackStrategy: RollbackPlan;
}
GET /api/schema/analyze/{projectId}
Response: {
analysis: SchemaAnalysis;
optimizations: Optimization[];
healthScore: number;
}
  • Local Processing: Schema analysis happens locally when possible
  • Encrypted Communication: All API calls use TLS 1.3
  • Data Retention: Conversation history encrypted and auto-deleted
  • Access Control: Integration with Convex project permissions
  • Validation: All generated code validated before execution
  • Sandboxing: Migration testing in isolated environments
  • Audit Logging: Complete history of schema changes and AI suggestions
  • Rollback Protection: Automatic backups before migrations
  • Output Filtering: Prevent generation of harmful or incorrect code
  • Confidence Scoring: AI indicates certainty levels for suggestions
  • Human Review: Critical changes require explicit developer confirmation
  • Fallback Modes: Graceful degradation when AI services unavailable
  • Chat Responses: < 2 seconds for simple queries
  • Code Generation: < 5 seconds for complex schemas
  • Migration Planning: < 10 seconds for typical changes
  • Schema Analysis: < 30 seconds for large projects
  • Concurrent Users: Support 1000+ simultaneous conversations
  • Project Size: Handle schemas with 100+ tables efficiently
  • Request Volume: Process 10,000+ API calls per minute
  • Storage: Efficient conversation context storage and retrieval
  • Memory: < 512MB per active conversation
  • CPU: Optimize for minimal impact on development experience
  • Network: Compress responses and cache common patterns
  • Storage: Efficient schema diff and analysis storage
  • AI response validation and consistency
  • Schema parsing and analysis accuracy
  • Migration generation correctness
  • Integration API functionality
  • End-to-end chat conversations
  • CLI and IDE extension workflows
  • Dashboard integration scenarios
  • Multi-project context handling
  • Response time benchmarking
  • Concurrent user load testing
  • Large schema analysis performance
  • Migration execution time validation
  • Developer workflow usability
  • Schema design accuracy validation
  • Migration safety and rollback testing
  • Learning curve and onboarding effectiveness
  • 95% migration success rate with zero data loss
  • < 3 second average response time for chat interactions
  • 90% code generation accuracy requiring no manual fixes
  • < 1% false positive rate for optimization suggestions
  • 8/10 average satisfaction rating from developer surveys
  • 50% reduction in schema-related development time
  • 80% of developers using the assistant for schema work
  • 90% feature adoption within teams that try it
  • 40% faster new developer onboarding to Convex
  • 60% reduction in schema-related support tickets
  • 25% improvement in query performance across projects
  • 15% increase in Convex customer satisfaction scores
  • Basic chat interface with schema generation
  • Simple migration planning
  • CLI integration
  • Core AI model training
  • Visual schema explorer
  • Performance optimization suggestions
  • IDE extensions (VS Code)
  • Dashboard integration

Phase 3: Advanced Capabilities (Months 7-9)

Section titled “Phase 3: Advanced Capabilities (Months 7-9)”
  • Complex migration scenarios
  • Multi-project context awareness
  • Advanced performance analytics
  • Team collaboration features

Phase 4: Optimization & Scale (Months 10-12)

Section titled “Phase 4: Optimization & Scale (Months 10-12)”
  • Performance improvements
  • Advanced AI capabilities
  • Enterprise features
  • Community pattern learning
  • AI Accuracy: Extensive testing and human review systems
  • Performance Impact: Careful resource management and caching
  • Integration Complexity: Phased rollout with feature flags
  • Data Security: Comprehensive security audit and encryption
  • Learning Curve: Interactive tutorials and onboarding guides
  • Trust Building: Transparent AI confidence indicators
  • Change Management: Gradual feature introduction
  • Support Load: Comprehensive documentation and training
  • Development Cost: Phased approach with early value delivery
  • Market Timing: Regular user feedback and iteration cycles
  • Competition: Focus on Convex-specific advantages
  • Resource Allocation: Cross-team collaboration and shared ownership

This PRD provides the foundation for building an AI-powered schema assistant that transforms how developers work with Convex databases, making complex schema management accessible, safe, and efficient.