Agent skill · product strategy · aiskillstore
ai-product
Every product will be AI-powered. The question is whether you'll
Why this skill is useful
Provides specific integration patterns and architectural guidance for AI product development that the AI wouldn't reliably generate on its own.
What it needs
Requires openai account access. About 11k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/ai-product