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From Monoliths to AI Proxies: Real-World Strategy for Testing and Evolving LLM Integrations

Ai Software Architecture Application Development Devops Software Architecture LLM Proxy Pattern AI Architecture Best-Practices Large Language Models LLM Integration Model Context Protocol (MCP) Testing Testing AI Systems
Integrating Large Language Models (LLMs) into production systems presents unique architectural, testing, and operational challenges. This article shares practical insights and solutions from real-world experience integrating LLMs into a customer interaction platform. It covers the evolution from a monolithic to a more modular AI Proxy architecture pattern, strategies for testing, deploying and monitoring LLMs, and the emerging Model Context Protocol (MCP) standard. Application developers and software architects will learn proven practices to build robust, reliable and responsible LLM-powered systems.

Kotlin Extensions for LangChain4j

Programming Ai Kotlin Coroutines Langchain4j
Discover Kotlin extensions for LangChain4j designed to transform the synchronous LangChain4j API into a modern, non-blocking experience with Kotlin Coroutines. Learn about key features including coroutine support for ChatLanguageModels, Kotlin Flow for streaming responses, external customizable prompt templates, and non-blocking document processing. Enhance your Kotlin programming skills and improve application efficiency by leveraging these powerful new tools.