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Adam-Guerin
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ai-product

Implement LLM integration patterns, RAG architecture, and prompt engineering for production-ready AI products.

1|Updated Feb 6, 2026
Search Tags:#prompt engineering#production#ai#llm#rag#product development
One-click install
npx skills add https://github.com/Adam-Guerin/Asmblr --skill ai-product-adam-guerin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-product
Source: https://github.com/Adam-Guerin/Asmblr/tree/main/skills/ai-product
Command: npx skills add https://github.com/Adam-Guerin/Asmblr --skill ai-product-adam-guerin

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the critical gap between AI product demos and robust production deployments, focusing on building reliable, scalable, and safe AI-powered features.

Core Features & Use Cases

  • LLM Integration Patterns: Implements best practices for integrating Large Language Models into products.
  • RAG Architecture: Provides guidance on Retrieval-Augmented Generation for accurate and context-aware responses.
  • Prompt Engineering: Focuses on treating prompts as code, including versioning and testing.
  • Production Readiness: Emphasizes validation, safety, and cost optimization over superficial demos.

Quick Start

Use the ai-product skill to implement structured output with validation for user feedback.

Frequently Asked Questions about ai-product

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build AI products that avoid common anti-patterns like demo-ware and context stuffing?▼

To build robust AI products, you must avoid demo-ware and context stuffing by implementing structured output, validation, and prompt versioning. This approach shifts focus from superficial demos to production-ready, scalable solutions.

How do I implement Retrieval-Augmented Generation (RAG) architecture for accurate LLM responses?▼

Implementing Retrieval-Augmented Generation (RAG) architecture integrates external context into LLM prompts, ensuring accurate and context-aware responses while avoiding the limitations of relying solely on model parametric knowledge.

What is the best way to treat prompt engineering as code for production AI applications?▼

The best way to treat prompt engineering as code is to apply versioning and testing to your prompts. This ensures consistent behavior across updates and enables reliable tracking of prompt performance in production environments.

Why does my LLM integration produce unreliable outputs in production compared to demos?▼

LLM integrations often fail in production due to missing validation and safety guardrails. Building production-ready AI requires structured output enforcement and robust prompt engineering to handle edge cases that demos ignore.

Can I use this approach to add streaming and structured output to existing LLM integrations?▼

Yes, you can implement LLM integration patterns that support both streaming and structured output with validation. These features are essential for creating responsive, reliable AI-powered features that scale safely in production environments.

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