Purpose

Teach the agent how to handle GenAI integration tasks — selecting models, building prompt templates, RAG pipelines, cost optimization, and validation workflows.

When to Apply

Use this Skill when the user asks to:

• integrate a GenAI API into an application
• design RAG workflows, embeddings pipelines, or agents
• build prompt templates or schema-validated prompts
• write automation for cost or token optimization
• add testing, logging, or observability around GenAI tasks

Instructions

1. Detect Task Intent

   • Identify if the request is about GenAI model selection, API integration, workflow design, or optimization.
   • If the task involves specific frameworks (Node/Python, serverless, Vercel/AWS), include relevant context.

2. Model & Provider Guidance

   • Recommend models according to cost, latency, context length, and compliance needs.
   • Prefer structured outputs (JSON schemas) and function/tool calling where appropriate.

3. Prompt Engineering

   • Generate prompt templates: system, developer, and user layers.
   • Use few-shot examples and explicit output schemas in prompts.

4. RAG & Embeddings

   • Break documents into chunks with semantic similarity filtering.
   • Outline vector store choice and search parameters (faiss/pinecone/weaviate).

5. Agent Workflows

   • If task requires agents, design tool use steps, fallback logic, and task decomposition.
   • Provide stepwise workflows for planning and execution.

6. Cost & Token Strategy

   • Suggest caching, batching, model tiering, and token budget limits.
   • Provide scripts or commands (in scripts/) for automation.

7. Validation & Safety

   • Add output validators (schema checks).
   • Mitigate prompt injection and unsafe operations.

Output Format Guidelines

• Include JSON-schema blocks where structured output is required.

Examples (Trigger Patterns)

• “Integrate LLM for customer support chatbot with RAG”
• “Design GenAI prompt templates for summarization API”
• “Automate token cost reduction for GenAI calls”