Building a Production LLM Application
You have built prompts, embeddings, RAG pipelines, function calling, caching layers, and guardrails. Separately. In isolation. Like practicing guitar scales without ever playing a song. This lesson is the song. You will wire every component from Lessons 01-12 into a single production-ready service. Not a toy. Not a demo. A system that handles real traffic, fails gracefully, streams tokens, tracks costs, and survives its first 10,000 users. Related: Phase 11 · 14 (MCP) for replacing bespoke tool schemas with a shared protocol; Phase 11 · 15 (Prompt Caching) for 50-90% cost reduction on stable prefixes. Both are expected in every serious 2026 production stack. Wire all Phase 11 components (prompts, RAG, function calling, caching, guardrails) into a single production-ready service. Implement streaming token delivery, graceful error handling, and request timeout management. Build observability into the application: request logging, cost tracking, latency percentiles, and error rate dashboards. Deploy the application with health checks, rate limiting, and a fallback strategy for provider outages. Building an LLM feature takes an afternoon. Shipping an LLM product takes months. The gap is not intelligence. It is infrastructure. Your prototype calls OpenAI, gets a response, prints it. Works on your laptop. Then reality arrives: A user sends a 50,000-token document. Your context window overflows. Two users ask the same question 4 seconds apart. You pay for…
Building a Production LLM Application: You have built prompts, embeddings, RAG pipelines, function calling, caching layers, and guardrails. Separately. In…
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