ai-product

# AI Product Development Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. ## Principles - LLMs are probabilistic, not deterministic | Description: The same input can give different outputs. Design for variance. Add validation layers. Never trust output blindly. Build for the edge cases that will definitely happen. | Examples: Good: Validate LLM output against schema, fallback to human review | Bad: Parse LLM response and use directly in database - Prompt engineering is product engineering | Description: Prompts are code. Version them. Test them. A/B test them. Document them. One word change can flip behavior. Treat them with the same rigor as code. | Examples: Good: Prompts in version control, regression tests, A/B testing | Bad: Prompts inline in code, changed ad-hoc, no testing - RAG over fine-tuning for most use cases | Description: Fine-tuning is expensive, slow, and hard to update. RAG lets you add knowledge without retraining. Start with RAG. Fine-tune only when RAG hits clear limits. | Examples: Good: Company docs in vector store, retrieved at query time | Bad: Fine-tuned model on company data, stale after 3 months - Design for latency | Description: LLM calls take 1-30 seconds. Users hate waiting. Stream responses. Show pro...