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 progress. Pre-compute when possible. Cache aggressively. | Examples: Good: Streaming response with typing indicator, cached embeddings | Bad: Spinner for 15 seconds, then wall of text appears
• Cost is a feature | Description: LLM API costs add up fast. At scale, inefficient prompts bankrupt you. Measure cost per query. Use smaller models where possible. Cache everything cacheable. | Examples: Good: GPT-4 for complex tasks, GPT-3.5 for simple ones, cached embeddings | Bad: GPT-4 for everything, no caching, verbose prompts