What security measures does this skill provide?

It implements defense-in-depth strategies including input sanitization to prevent prompt injection and output validation to catch harmful or hallucinated content before it reaches the user.

Can this skill help reduce my LLM API bill?

Yes, it focuses on cost optimization patterns, such as avoiding context window stuffing and using prompt engineering techniques that reduce token consumption by up to 80%.

How does this skill improve AI output reliability?

It enforces the use of structured output validation and schema-based parsing (JSON mode) to ensure LLM responses remain consistent and don't break application logic.

Does it support Retrieval-Augmented Generation (RAG)?

Absolutely. It provides specific patterns for RAG architecture, including how to efficiently manage context and validate facts within the LLM response.

AI Product Engineering

Name: AI Product Engineering
Author: claudiodearaujo

byclaudiodearaujo

•

Ciencia de Datos y ML

Develops production-ready AI features using advanced LLM integration patterns, prompt engineering, and cost-optimization strategies.

The AI Product Engineering skill transforms Claude into a production-focused architect who prioritizes reliability over simple demos. It provides domain-specific expertise in Retrieval-Augmented Generation (RAG), prompt versioning, and structured output validation to ensure AI features remain stable in real-world environments. Use this skill to move beyond brittle implementations by adopting asynchronous processing, cost-efficient token management, and safety layers that protect against hallucinations and prompt injection.

Características Principales

01Provides patterns for RAG architecture and token-efficient context management

02Optimizes prompt engineering to balance performance with significant cost reduction

03Implements structured output validation using JSON mode and function calling

04Configures streaming architectures to improve UX and reduce perceived latency

05Enforces defense-in-depth safety layers for input sanitization and fact-checking

061 GitHub stars

Casos de Uso

01Building cost-effective AI features for high-traffic applications

02Architecting scalable RAG systems for large-scale enterprise documentation

03Transitioning a brittle AI proof-of-concept into a robust production service

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/sistema-de-narra-o-de-livro ai-product

For use in Claude.ai and ChatGPT

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