Does it support streaming responses?

Yes, it advocates for streaming LLM responses with progress indicators to improve the user experience and reduce perceived latency in AI applications.

Can this skill help reduce my AI API costs?

Yes, it provides patterns for cost optimization, including token calculation before sending requests and efficient context management to minimize overhead.

Is this skill useful for RAG implementations?

Absolutely; it covers RAG architecture best practices and warns against 'context window stuffing' to ensure relevant data retrieval without diluting context.

How does this skill improve AI output reliability?

It enforces structured output using function calling or JSON mode with strict schema validation to prevent inconsistent formatting and parsing errors.

What are the common 'sharp edges' this skill helps avoid?

It identifies and mitigates critical risks like trusting unvalidated LLM output, lack of input sanitization, and making synchronous blocking calls in web handlers.

AI Product Engineering

Name: AI Product Engineering
Author: claudiodearaujo

byclaudiodearaujo

•

Ciencia de Datos y ML

Builds production-ready AI features using robust integration patterns, RAG architectures, and cost-optimized prompt engineering.

The AI Product Engineering skill bridges the gap between proof-of-concept demos and stable production environments by implementing proven LLM integration patterns. It guides developers through structured output validation, streaming UI implementations, and prompt versioning while avoiding common pitfalls like context window stuffing or unsanitized inputs. This skill is essential for building trustworthy, scalable, and cost-efficient AI-powered applications that prioritize user safety and system reliability.

Características Principales

01Configures streaming responses to improve perceived latency

02Implements structured output with JSON/schema validation

03Optimizes prompt engineering for cost reduction and performance

04Sets up safety layers to catch hallucinations and harmful outputs

051 GitHub stars

06Establishes robust RAG (Retrieval-Augmented Generation) architectures

Casos de Uso

01Optimizing LLM API costs through smarter prompt management and token tracking

02Converting an unstable AI demo into a production-hardened application

03Implementing reliable data extraction from unstructured AI responses

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

Download Skill