4 articles tagged #production.
Practices and tools for monitoring, tracing, and debugging AI systems in production, covering token metrics, latency, response quality, costs, and hallucination detection.
Patterns and frameworks for coordinating multiple AI models, tools, and data sources in production pipelines, managing flow between components, memory, and error recovery.
Architecture design for scaling a personal second brain to a production system with AWS serverless — from the current prototype to specialized use cases in legal, research, and community building.
Production-ready serverless backend for a personal knowledge graph — DynamoDB, Lambda, Bedrock, MCP, Step Functions. The implementation of the architecture described in the 'From Prototype to Production' essay.