Guide details full-stack LLM application development and production best practices

This guide outlines the development of production-grade LLM applications, emphasizing a full-stack approach beyond simple API calls. It details core architectural components including prompt layers with system prompts and context injection, context management strategies like sliding windows and RAG, and tool use for function calling. The guide also covers essential production considerations such as streaming, caching, structured output, cost optimization, and a checklist for deployment. AI

IMPACT Provides a structured approach for developers to build and deploy robust LLM applications, focusing on cost and performance.

RANK_REASON This is a guide on how to build LLM applications, not a release of a new model or research.

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• LLM
• Python

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