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ai

Integrate LLMs, RAG pipelines, vector stores, and deployment tooling for AI systems.

25|1|Updated Jan 27, 2026
Search Tags:#deployment#embeddings#ai#rag#vector-store#ml-engineering#llms
One-click install
npx skills add https://github.com/hyperb1iss/hyperskills --skill ai-hyperb1iss
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai
Source: https://github.com/hyperb1iss/hyperskills/tree/main/skills/ai
Command: npx skills add https://github.com/hyperb1iss/hyperskills --skill ai-hyperb1iss

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps teams build production AI systems by integrating LLMs, RAG, embeddings, and deployment tooling, enabling scalable AI-enabled products.

Core Features & Use Cases

  • End-to-end AI development patterns: prompting, orchestration, document ingestion and retrieval, and model deployment.
  • RAG-enabled workflows: build retrieval-augmented generation pipelines with vector stores (LlamaIndex, Qdrant, Pinecone) and evaluation.
  • Experimentation and deployment tooling: track experiments with MLflow/W&B, and deploy with BentoML or vLLM.

Quick Start

Configure a simple AI pipeline that ingests documents, indexes them in a vector store, runs a retrieval step, and serves a model for user queries.

Frequently Asked Questions about ai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a production AI system with RAG and vector stores?▼

Build a production AI system with RAG by integrating LLMs, document ingestion, vector stores, and deployment tooling. This approach covers end-to-end AI workflows from prompting to deployment in real-world contexts.

What's the best way to orchestrate LLM workflows with LangGraph and LlamaIndex?▼

Orchestrate LLM workflows with LangGraph and LlamaIndex by building retrieval-augmented generation pipelines that handle document ingestion, index data in vector stores, and run retrieval steps for user queries.

Can I deploy LLMs and track ML experiments using MLflow and BentoML?▼

Yes, you can deploy LLMs and track ML experiments using MLflow and BentoML. Track experiments with MLflow or W&B, then deploy models in real-world contexts using BentoML or vLLM.

Does this AI deployment approach work with Qdrant and Pinecone for embeddings?▼

This AI deployment approach works with Qdrant and Pinecone by using them as vector stores for your embeddings. You build RAG-enabled workflows that index documents and run retrieval steps within these vector databases.

When do I need a modular AI stack with DSPy and MCP for production environments?▼

You need a modular AI stack with DSPy and MCP for production environments when building scalable AI-enabled products that require end-to-end AI development patterns across prompting, orchestration, retrieval, and deployment.

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