October 14, 2025

Next Word Predictor

Type Faster. Predict Smarter.



The Problem:

  • Mobile typing is 41% slower than desktop
  • Users make 2-3x more errors on mobile
  • Current solutions are slow and resource-intensive


Our Solution: Lightning-fast text prediction powered by N-gram models

How It Works

N-gram Language Model with Stupid Backoff

The Algorithm in 3 Steps:

1. Capture Context

User types: "I love"
Extract: last 2 words → ["I", "love"]

2. Search Training Data

  • Trigram (n=3): Find “I love [?]” patterns
  • Bigram (n=2): Find “love [?]” patterns
  • Unigram (n=1): Find most common words

3. Rank & Predict

P(word|context) = Count(trigram) / Count(context)
Apply backoff: trigram → bigram → unigram

Technical Details:

  • Training: 850K+ sentences (blogs, news, Twitter)
  • Model: 420K n-grams, 30K vocabulary
  • Size: 33 MB (deployable anywhere)
  • Speed: < 0.5 seconds per prediction


Key Innovation: Stupid Backoff smoothing

  • Fast (no normalization needed)
  • Effective (handles unseen n-grams)
  • Scalable (works with large corpora)

The Product Demo

✨ Key Features:

  1. Real-Time Predictions
    • Updates as you type
    • No page refresh needed
  2. Interactive Interface
    • Click predictions to insert
    • Visual confidence scores
    • Word cloud visualization
  3. Performance Dashboard
    • Live statistics
    • Word/character counts
    • Prediction timing
  4. Sample Text Buttons
    • Quick testing
    • Demo scenarios

📱 User Flow:

Step 1: Type text
  "The weather is"
       ↓
Step 2: Get predictions
  1. beautiful (42%)
  2. nice (28%)
  3. getting (15%)
       ↓
Step 3: Click to insert
  "The weather is beautiful"


🎯 Use Cases:

  • Mobile keyboards
  • Email assistants
  • Chatbots
  • Accessibility tools
  • Writing apps