Large Language Models have transformed how we think about AI applications, but building production-ready systems requires more than just an API call to GPT-4. In this post, I’ll share some lessons learned from deploying LLM-powered applications at scale.
The Reality Check
When you’re prototyping, it’s easy to get excited about the capabilities of modern LLMs. But production is a different beast. You need to think about latency, cost, reliability, and security—all while maintaining the quality that makes these models useful in the first place.
Key Considerations
Context Management: The way you structure prompts and manage context windows can make or break your application. I’ve seen teams spend months optimizing their RAG (Retrieval-Augmented Generation) pipelines because they didn’t think through their chunking strategy upfront.
Cost Optimization: Token costs add up fast. Caching strategies, smart batching, and choosing the right model for each task can reduce costs by 10x or more.
Observability: You need to see what’s happening inside your LLM calls. Logging, tracing, and evaluation metrics are essential for debugging and improvement.
The Path Forward
The LLM landscape is evolving rapidly, but the fundamentals of good software engineering still apply. Start simple, measure everything, and iterate based on real user needs.
More details coming in future posts!