Design GenAI integration workflows with model selection, RAG pipelines, and guardrails.
npx skills add https://github.com/neoju/actz --skill genai-integration
Please help me install this Agent Skill. Skill: genai-integration Source: https://github.com/neoju/actz/tree/main/.agent/skills/genai-integration Command: npx skills add https://github.com/neoju/actz --skill genai-integration
Designing GenAI integrations is complex and often error-prone without a structured approach. This skill provides expert guidance for model selection, prompt engineering, and end-to-end pipelines (including RAG and embeddings), along with cost controls and observability to ensure robust, compliant deployments.
Provide a concrete GenAI integration plan for a sample app and begin implementing the recommended prompts, pipelines, and observability checks.
High-intent search queries and answers about installing and using this skill.
Designing a RAG pipeline involves selecting embedding models, applying chunking strategies, and configuring vector stores. This integration provides structured guidance for building RAG and embeddings pipelines across Node, Python, serverless, and cloud deployments.
GenAI model selection balances cost, latency, context length, and compliance needs. This guidance evaluates your specific project constraints to recommend the optimal model and design explicit prompt templates with structured output schemas.
Prompt engineering templates separate system, developer, and user layers with explicit output schemas. You implement validation checks and safety guardrails to prevent prompt injection and ensure compliant, structured responses.
GenAI cost optimization uses caching, batching, and tiering strategies to reduce token usage and API calls. This approach integrates validation and observability checks to maintain robust, compliant deployments while controlling expenses.
Observability and guardrails are essential for serverless GenAI deployments to monitor performance and prevent unsafe operations. This integration provides end-to-end workflows with validation, testing, and fallback logic for compliant cloud applications.
HechaoYannetGenerate a master architecture document from GDDs, ADRs, and governance constraints.
alirezarezvaniOptimize LLM prompts and evaluate RAG systems for production AI systems.
zhangzhang-111-iDesigns RAG pipelines with chunking, embedding, retrieval, and evaluation strategies.
JCETools-PetraDesign RAG pipelines with chunking, embedding, retrieval, and evaluation metrics.
