The original title is "Integrating LLMs into Production: Practical Patterns and Pitfalls"

Short summary

A practical guide to deploying LLMs in production covering architecture (queue-based async pipelines), cost control (token limits, Redis caching, batching), safety (input sanitization, output filtering, human-in-the-loop), and observability (Prometheus, Grafana, OpenTelemetry). Includes a real-world case study of a help-center assistant handling ~150 QPS at <$0.02 per 1k tokens using GPT-4o-mini with RAG. Common pitfalls like unbounded input, missing retries, and no observability are tabulated with fixes.

• •Covers LLM production architecture: async queues, caching, safety layers, observability
• •Real case study: 150 QPS help-center assistant using GPT-4o-mini + RAG at <$0.02/1k tokens
• •Tabulated pitfalls and fixes for common production issues

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