Next-Word Prediction System Design

1. The Question

Design a next-word prediction system for keyboard typing. The system should suggest the most likely next word given the user’s typed context. Consider requirements such as latency, language support, modality (keyboard typing), cold-start availability of data, and accuracy as the primary success metric. Cover data collection and processing (including ethical user experiments), tokenization tradeoffs, modeling progression from n-gram to Transformer encoding-only models trained with self-supervised next-token objectives, evaluation on held-out test sets, deployment (API, quantization/ONNX), and production monitoring and scaling.

2. Clarifying Questions

• Target platforms: mobile, desktop, or both?
• Supported languages and whether multilingual support is required.
• Max acceptable latency (ms) for suggestions to appear.
• Memory/compute limits on client devices.
• Privacy constraints (can typed text leave device? differential privacy required?).
• Cold-start definition: no historical data per new user, but global corpora available?
• Success metric details: top-1 accuracy, top-k accuracy, or BLEU-like metrics?

Notes: For this design assume mobile-first with a 50–100ms latency target for suggestions, support for English initially, cold-start at per-user level but global training data available, and accuracy (top-1) is primary.

3. Requirements

Functional requirements:

• Given recent typed tokens (context), return top-k next-word suggestions.
• Support real-time suggestions while typing on keyboard (low latency).
• Update model periodically with new data; allow personalization.

Non-functional requirements:

• Latency: 50–100ms round-trip for suggestion generation on server-assisted flow; <=5ms additional client processing.
• Accuracy: maximize top-1 accuracy; report top-3/top-5 as secondary.
• Availability: 99.9% service availability.
• Privacy: minimize sensitive data transmission; allow on-device inference and apply differential privacy for aggregated training if needed.
• Throughput: handle peak QPS consistent with user base (see scale estimates).

4. Scale Estimates

Assumptions:

• Active users: 10M daily active users (DAU).
• Average sessions per user: 5 per day.
• Average suggestion requests per session: 50.

Calculations:

• Requests/day = 10M * 5 * 50 = 2.5B requests/day.
• Requests/sec (avg) ≈ 2.5B / 86400 ≈ 28935 RPS.
• Peak factor 3 → target peak RPS ≈ 90k.

Storage:

• Global training corpus: several TB (web + public corpora + anonymized keyboard logs).
• Model size examples: n-gram model compressed few hundred MB; Transformer baseline (e.g., 100M params) ~400MB FP32; quantized to 8-bit or 4-bit reduces size significantly.

Implications: need horizontally scalable inference fleet, efficient batching, optional on-device models for latency reduction.

5. Data Model

Primary data entities:

• RawEvent: { user_id (hashed), timestamp, device_type, language, input_text_segment (tokenized), client_metadata }
• AggregatedStats: n-gram counts, token frequencies, co-occurrence tables.
• ModelArtifact: { model_id, version, params_meta, training_data_hash, metrics }
• EvaluationSet: held-out examples with context → next-token labels.

Privacy & retention:

• Store only hashed user IDs and strip PII. Enforce data retention policies and opt-out handling.

Data splits:

• Construct training/validation/test splits by sampling users (user-disjoint splits) to avoid data leakage and better measure generalization.
• For cross-validation, use k-fold at corpus-level or leave-one-user-out for personalization evaluation.

6. API Design

Two API modes:

1. Server-assisted REST/HTTP (polling):

• POST /v1/predict

  • Body: { client_id, context_tokens: ["I","am"], language: "en", k: 3, client_constraints }
  • Response: { suggestions: [{token: "happy", score: 0.32}, ...], model_version }

• Pros: simple, easy to cache and scale.

• Cons: higher latency vs persistent connections.

2. WebSocket / gRPC streaming:

• Establish persistent connection; stream keystrokes and receive async suggestions.
• Lower per-request handshake overhead; better for very low-latency interactive flows.

Client considerations:

• Client should perform lightweight filtering (e.g., profanity lists) and rendering. Include model_version to support A/B testing.
• Use TLS for transport security and token-based auth.

7. High-Level Architecture

Components:

• Data Collector (client SDK): collects typed segments with user consent, locally buffers and uploads with privacy protections.
• Ingestion & ETL: cleans, deduplicates, anonymizes; produces training corpora and n-gram counts.
• Offline Training: pipelines to train n-gram baselines and Transformer models; includes validation and metrics tracking.
• Model Registry & Artifact Store: versioned models, metadata, and quantized builds (ONNX/TFLite).
• Inference Cluster: autoscaled fleet behind load balancers; supports REST and gRPC endpoints, efficient batching, caching of recent context results.
• On-device Runtime: lightweight quantized model for offline/low-latency inference (optional).
• Monitoring & Logging: latency, error rates, suggestion accuracy, model drift metrics; Grafana/Prometheus, centralized logging.

Dataflow:

Client → Data Collector → ETL → Training → Model Registry → Inference Cluster (or on-device).

8. Detailed Design Decisions

Tokenization strategy:

• Word-level encoding:

  • Pros: intuitive, fewer tokens for common words, fast decoding.
  • Cons: large vocab, OOV issues for rare words and misspellings.

• Character-level encoding:

  • Pros: unlimited vocab, handles typos and unseen words.
  • Cons: longer sequences, harder to learn long-range semantics.

• Byte-Pair Encoding (BPE) / Subword:

  • Pros: balance between vocab size and open-vocabulary handling; common for Transformers.
  • Cons: choice of merge ops affects granularity; slightly more complex.

Recommendation: use subword (BPE or SentencePiece) for Transformer models; provide optional word-level n-gram baseline for efficiency.

Modeling progression:

1. N-gram baseline:

   • Train word-level n-gram with Kneser-Ney smoothing.
   • Use backoff to lower-order n-grams for OOV.
   • Extremely fast and small; good cold-start and interpretable baseline.

2. Transformer encoding-only model:

   • Inputs: token embeddings + positional encodings.
   • Architecture: multi-head self-attention layers followed by feed-forward layers.
   • Training objective: next-token prediction (causal/self-supervised). Use teacher forcing: for each position predict next token.
   • Batching: create training examples by sliding windows up to context_size; each sequence produces multiple training labels.

Training details:

• Optimization: AdamW, learning rate schedule (warmup + cosine decay).
• Regularization: weight decay, dropout.
• Personalization: fine-tune per-user adapter layers or use lightweight on-device cache with rescoring.

Serving optimizations:

• Quantize models (8-bit, 4-bit) for reduced memory and faster inference.
• Convert to ONNX/TFLite for platform-specific acceleration.
• Use caching of common contexts and response shards.
• Use shallow prefix tree rescoring combining n-gram counts with model probabilities for very low-latency suggestions.

9. Bottlenecks & Scaling

Potential bottlenecks:

• Latency: Transformer inference can be slow; mitigate via model compression, shorter context windows, on-device inference, or hybrid n-gram fallback.
• Throughput: high QPS requires batching and autoscaling; batching introduces latency tradeoffs.
• Data privacy & compliance: restrictions may limit data availability and personalization.
• Model drift: language usage changes over time; need continual evaluation and retraining.

Scaling strategies:

• Horizontal scaling of inference nodes behind load balancers.
• Use GPU/TPU-backed instances for heavy models; CPU-optimized quantized models for cheap, low-latency inference.
• Autoscaling policies based on latency SLOs and queue depth.
• Edge/on-device models to reduce server load and latency for common cases.
• CDN-like caching for repeated contexts and suggestions.

10. Follow-up Questions / Extensions

• How to personalize suggestions while preserving privacy? (federated learning, on-device fine-tuning, or per-user lightweight caches)
• Support for multiple languages and code-switching?
• How to handle offensive or sensitive suggestions? (filtering, safety classifiers, context-aware suppression)
• Can we provide multi-token completions or whole-phrase suggestions?
• How to integrate user feedback signals (accept/reject) into online learning?
• A/B test strategies to measure impact on typing speed and user satisfaction.

11. Wrap-up

Design summary:

• Start with a robust n-gram baseline for cold-start and low-latency needs.
• Move to a subword-tokenized Transformer for better accuracy, trained with next-token self-supervised objective.
• Protect privacy via anonymization, user-disjoint splits, and consider federated/on-device approaches.
• Deploy via an autoscaled inference cluster with options for on-device quantized models; expose REST and WebSocket APIs.
• Monitor accuracy, latency, and safety metrics; iterate with new data and model versions.

Key tradeoffs: accuracy vs latency, centralization vs privacy, and model complexity vs cost.