Design AI prompts and system architectures for LLM tasks.
npx skills add https://github.com/CyangZhou/-2--Project-Yunshu- --skill ai-llm-engineer
Please help me install this Agent Skill. Skill: ai_llm_engineer Source: https://github.com/CyangZhou/-2--Project-Yunshu-/tree/main/.trae/skills/ai_llm_engineer Command: npx skills add https://github.com/CyangZhou/-2--Project-Yunshu- --skill ai-llm-engineer
This skill guides the design of AI architectures, prompt construction, and the creation of RAG and LangChain applications to streamline scalable AI workflows.
Provide a structured prompt to initiate a basic retrieval-augmented generation workflow.
High-intent search queries and answers about installing and using this skill.
Build RAG pipelines by designing retrieval-augmented generation workflows with LangChain integration. You initiate a basic workflow by providing a structured prompt to retrieve and generate knowledge-based responses.
Structure prompts for multi-model strategies using chain-of-thought and safety defenses. This ensures model-specific optimization and supports evaluation and iterative refinement across different LLMs.
Yes, you can use LangChain integration to build end-to-end AI pipelines for real-world knowledge tasks. It scales from small experiments to production-grade implementations for robust LLM orchestration.
Your RAG system needs structured prompt engineering to optimize prompt effectiveness and reduce hallucinations. Structured topology ensures reliable retrieval and generation across complex AI architectures.
Chain-of-thought strategies are applicable from small experiments to production-grade implementations. They improve prompt reliability and reasoning depth, supporting iterative evaluation and refinement.
tluisguereroItesmValidate required fields, formats, and value bounds before invoking tools.
yoonhwanAutomate iterative code optimization loops with structured JSON experiment results.
figulazmiOrchestrate RAG, LangChain, and agents for end-to-end AI workflows.
bcastelinoImplement production-grade LLM applications, RAG systems, and agent architectures.
