diegosouzapw 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
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.
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.
Prompt Engineering
- Generate prompt templates: system, developer, and user layers.
- Use few-shot examples and explicit output schemas in prompts.
RAG & Embeddings
- Break documents into chunks with semantic similarity filtering.
- Outline vector store choice and search parameters (faiss/pinecone/weaviate).
Agent Workflows
- If task requires agents, design tool use steps, fallback logic, and task decomposition.
- Provide stepwise workflows for planning and execution.
Cost & Token Strategy
- Suggest caching, batching, model tiering, and token budget limits.
- Provide scripts or commands (in
scripts/) for automation.
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”