AI-Powered Predictive Data Input for Mobile Forms
Predictive input isn't iOS/Android autocorrect. It's contextual prediction of field values based on user history, current context, and patterns. User starts typing a recipient name—the app already knows they typically transfer to the same person on Friday evenings.
Prediction Sources
User history. Most powerful signal. Frequent recipients, typical amounts by day of week, recurring payment purposes—all patterns extracted from local or server history.
Session context. If user arrived from a push notification "time to pay utilities", the first field of payment form reasonably pre-fills with utility company details.
LLM-generation from partial input. User types "for offi"—model predicts "for office rent, November 2025". Implemented via streaming completions with a small, fast model (gpt-4o-mini) with low latency.
Predictive Implementation with Debounce
Querying LLM at every keystroke is wasteful. Standard approach: debounce 300–500ms, request sent only when user pauses.
// iOS — Swift, SwiftUI
class PredictiveInputViewModel: ObservableObject {
@Published var suggestions: [String] = []
private var debounceTask: Task<Void, Never>?
func onTextChange(_ text: String, fieldType: FormFieldType, context: FormContext) {
debounceTask?.cancel()
guard text.count >= 3 else { suggestions = []; return }
debounceTask = Task {
try? await Task.sleep(nanoseconds: 400_000_000) // 400ms debounce
guard !Task.isCancelled else { return }
let predictions = await fetchPredictions(text: text, fieldType: fieldType, context: context)
await MainActor.run { self.suggestions = predictions }
}
}
private func fetchPredictions(text: String, fieldType: FormFieldType, context: FormContext) async -> [String] {
// First search local history (fast, no network)
let localMatches = userHistory.search(query: text, fieldType: fieldType)
if localMatches.count >= 3 { return Array(localMatches.prefix(3)) }
// If insufficient — request AI
return await aiSuggestionService.predict(text: text, fieldType: fieldType, context: context)
}
}
Local Cache vs Server Predictions
Simple predictions (frequently used recipients, typical amounts)—store locally, don't query network. SQLite + FTS5 for fast history search delivers latency < 5ms.
LLM predictions justified only for complex text fields (payment purpose, address, description). Local search won't produce quality results there.
Prediction UX
Predictions display as chip suggestions below field or inline dropdown—not in system suggestion bar (OS-controlled, not app-controlled). Tap on suggestion instantly fills field without animation. Critical: predictions must work on slow connections, so local cache isn't optional but necessary.
Timeframe Estimates
Predictions from local history—2–3 days. Hybrid system with LLM for text fields and debounce—3–5 days.