Design and implement LLM systems with RAG pipelines, agent frameworks, and guardrails.
npx skills add https://github.com/grasberg/sofia --skill ai-engineer-grasberg
Please help me install this Agent Skill. Skill: ai-engineer Source: https://github.com/grasberg/sofia/tree/main/workspace/skills/ai-engineer Command: npx skills add https://github.com/grasberg/sofia --skill ai-engineer-grasberg
Building robust, production-grade LLM-powered systems often requires integrating RAG pipelines, agent frameworks, memory, and strict guardrails; this Skill provides a structured blueprint to design, implement, and deploy reliable AI applications.
Design and deploy a production-grade LLM system with RAG pipelines, agent orchestration, embeddings, and guardrails for a specified use case.
High-intent search queries and answers about installing and using this skill.
Build a production-grade RAG pipeline by integrating chunking strategies, generating embeddings, and storing them in vector stores for fast retrieval. This approach ensures accurate information retrieval and automates knowledge work with reliable LLM workflows.
Orchestrate agent frameworks by configuring tool-calling capabilities, establishing multi-agent coordination, and implementing long-term memory. This allows safe execution of complex tasks and supports scalable deployment patterns with fallback chains for reliable automation.
Add guardrails to an LLM system by implementing evaluation metrics and deployment patterns that manage failures, latency, and cost. This structured blueprint ensures reliable AI applications by including fallback chains and strict execution boundaries.
Yes, you can design an enterprise search system with secure access controls using this approach. It supports retrieval-augmented generation, long-term memory, and scalable deployment patterns tailored for data-rich workflows and decision support.
You need fallback chains in LLM deployment patterns when building reliable AI systems for data-rich workflows. They handle failures and latency by providing alternative execution paths, ensuring continuous operation during information retrieval and automation tasks.
patrykkopycinskiSet up a RAG pipeline with Elasticsearch for vector retrieval.
latestaiagentsEvaluate RAG systems with retrieval metrics and LLM-judged generation quality.
EanLeeDesign autonomous AI agents with tool use, memory, planning, and multi-agent orchestration.
latestaiagentsRoute LLM tasks to models based on complexity, cost, and latency.
