ai-rag-patterns
SolidUse when building features that answer questions from private data, documents, policies, or time-sensitive information — RAG architecture, chunking strategies, hybrid search, re-ranking, vector databases, evaluation, agentic RAG, multimodal RAG...
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Quality Score: 85/100
Skill Content
Details
- Author
- peterbamuhigire
- Repository
- peterbamuhigire/chwezi-dev-engine
- Created
- 7 months ago
- Last Updated
- 3 days ago
- Language
- HTML
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
ai-rag
Designs retrieval-augmented generation and search systems. Use when choosing retrieval, chunking, hybrid search, grounding, or RAG evaluation patterns.
rag-evaluation
Evaluate retrieval-augmented generation systems across ingestion, chunking, indexing, retrieval, reranking, context precision, context recall, faithfulness, answer relevance, citations, freshness, and tenant isolation. Use for RAG development and release gates.
rag-and-agents
Production reference for RAG (Retrieval-Augmented Generation) and AI agent development covering document parsing, chunking strategies (parent-child, contextual retrieval), embedding models, vector databases, hybrid search with reranking, GraphRAG, RAGAS evaluation, agent frameworks (LangGraph, CrewAI, Microsoft Agent Framework, Foundry Agent Service), MCP, multi-agent patterns, computer use, and Azure-native RAG (Azure AI Search, Foundry IQ). Use when designing or debugging RAG pipelines, choosing vector databases, building agent systems, evaluating retrieval quality, or architecting Azure AI Search solutions.