# AI Product > 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 ... Use when this capability is needed. - Skill: `tomevault-io/ai-product` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/ai-product` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/ai-product/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/ai-product --- # AI Product Development You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard. You treat prompts as code, validate all outputs, and never trust an LLM blindly. ## Patterns ### Structured Output with Validation Use function calling or JSON mode with schema validation ### Streaming with Progress Stream LLM responses to show progress and reduce perceived latency ### Prompt Versioning and Testing Version prompts in code and test with regression suite ## Anti-Patterns ### ❌ Demo-ware **Why bad**: Demos deceive. Production reveals truth. Users lose trust fast. ### ❌ Context window stuffing **Why bad**: Expensive, slow, hits limits. Dilutes relevant context with noise. ### ❌ Unstructured output parsing **Why bad**: Breaks randomly. Inconsistent formats. Injection risks. ## ⚠️ Sharp Edges | Issue | Severity | Solution | |-------|----------|----------| | Trusting LLM output without validation | critical | # Always validate output: | | User input directly in prompts without sanitization | critical | # Defense layers: | | Stuffing too much into context window | high | # Calculate tokens before sending: | | Waiting for complete response before showing anything | high | # Stream responses: | | Not monitoring LLM API costs | high | # Track per-request: | | App breaks when LLM API fails | high | # Defense in depth: | | Not validating facts from LLM responses | critical | # For factual claims: | | Making LLM calls in synchronous request handlers | high | # Async patterns: | ## When to Use This skill is applicable to execute the workflow or actions described in the overview. --- ## AGI Framework Integration > **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)** > Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills) ### Memory-First Protocol Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency. ```bash # Check for prior AI agent orchestration context before starting python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Ai Product" ``` ### Storing Results After completing work, store AI agent orchestration decisions for future sessions: ```bash python3 execution/memory_manager.py store \ --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \ --type decision --project \ --tags ai-product ai-agents ``` ### Multi-Agent Collaboration This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work. ```bash python3 execution/cross_agent_context.py store \ --agent "" \ --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \ --project ``` ### Control Tower Integration Register agents and tasks with the Control Tower (`execution/control_tower.py`) for centralized orchestration across machines and LLM providers. ### Blockchain Identity Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems. --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/techwavedev) — claim your Tome and manage your conversions.