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The article argues that the biggest AI shift is not larger models but better systems around them, such as context, model routing, caching, agent workflows, and evaluation, making the model the engine and the system the product.
A practitioner shares 13 hard-won lessons from building production AI voicebots/chatbots for technical field service, covering document ETL, cost reduction, and agent orchestration.
A discussion on strategies for managing token budgets when deploying multiple AI agents in production, covering cost and efficiency considerations.
A discussion on AI agent observability highlights unpredictable cost variations and dangerous failure modes like unauthorized database deletes, prompting questions about production handling strategies beyond basic logging.
Akshay Pachaar outlines essential skills for AI engineers beyond prompt engineering, including caching strategies, observability, and cost attribution.
This post outlines a comprehensive 9-layer AI production architecture, emphasizing components like RAG pipelines, security guards, observability, and evaluation to distinguish robust production systems from simple demos.
LangChain emphasizes the importance of evaluating AI applications before deployment and monitoring them afterward to continuously improve model performance.
Perplexity has released its internal Agent Skills building handbook, proposing a new developer mindset distinct from traditional software engineering, emphasizing context management and implicit pattern matching principles for AI agents.
This article introduces 7 production-ready skills from the Hermes Skills Hub, covering the full lifecycle from tool integration and structured output to deployment, observability, and security.