ai-native-developmentlisted

# AI-Native Development ## Overview AI-Native Development focuses on building applications where AI is a first-class citizen, not an afterthought. This skill provides comprehensive patterns for integrating LLMs, implementing RAG (Retrieval-Augmented Generation), using vector databases, building agentic workflows, and optimizing AI application performance and cost. **When to use this skill:** - Building chatbots, Q&A systems, or conversational interfaces - Implementing semantic search or recommendation engines - Creating AI agents that can use tools and take actions - Integrating LLMs (OpenAI, Anthropic, open-source models) into applications - Building RAG systems for knowledge retrieval - Optimizing AI costs and latency - Implementing AI observability and monitoring --- ## Why AI-Native Development Matters Traditional software is deterministic; AI-native applications are probabilistic: - **Context is Everything**: LLMs need relevant context to provide accurate answers - **RAG Over Fine-Tuning**: Retrieval is cheaper and more flexible than fine-tuning - **Embeddings Enable Semantic Search**: Move beyond keyword matching to understanding meaning - **Agentic Workflows**: LLMs can reason, plan, and use tools autonomously - **Cost Management**: Token usage directly impacts operational costs - **Observability**: Debugging probabilistic systems requires new approaches - **Prompt Engineering**: How you ask matters as much as what you ask --- ## Core Concepts ### 1. Embeddin