Taking Large Language Models from playground to production is a completely different beast. While fine-tuning on your laptop feels awesome, production requires thinking about reliability, cost, latency, and monitoring. Let me share what I've learned.
The Production LLM Stack
A production LLM system needs more than just an API call. You need:
- API Layer: FastAPI or similar for request handling, rate limiting, authentication
- Model Serving: vLLM, TensorRT-LLM, or cloud-managed solutions (OpenAI API, Claude)
- Caching: Redis for prompt/response caching to reduce costs and latency
- Monitoring: Track token usage, latency, costs, and error rates
- Versioning: Model versioning with rollback capabilities
- Safety: Input validation, output filtering, toxicity detection
Cost Optimization Strategies
Token costs add up fast. Here's how to optimize:
- Prompt Caching: Use prompt caching when the context is large and static
- Smaller Models: Fine-tune smaller models for specific tasks instead of using GPT-4
- Batch Processing: Process requests in batches during off-peak hours
- Input Truncation: Intelligently truncate irrelevant context
- Model Routing: Route simple queries to faster, cheaper models
Handling Hallucinations
LLMs confidently generate wrong information. Combat this with:
- RAG: Ground your model in actual data with Retrieval-Augmented Generation
- Fact Checking: Verify generated facts against a knowledge base
- Output Validation: Use structured outputs and semantic validation
Monitoring & Observability
What you can't measure, you can't improve. Track:
- Token usage and costs per user/request
- Latency percentiles (p50, p95, p99)
- Error rates and categories
- User satisfaction scores
- Model drift and output quality degradation
Key Takeaways
Production LLM systems require:
- 🔧 Proper engineering infrastructure
- 💰 Cost management from day one
- 📊 Comprehensive monitoring
- 🛡️ Safety mechanisms
- ♻️ Continuous improvement loops
Next Step: Start with a small RAG system, add monitoring early, and iteratively optimize based on real usage patterns. Don't over-engineer initially.